At the end of the course, students should be able to:
LO1: Create and Manipulate Data Structures
LO2: Apply the most appropriate sorting and search algorithms for a specific problem
LO3: Analyze the complexity and performance of an algorithm
LO4. Identify, implement, and analyze the most appropriate data structures and algorithms for a certain problem

S1. The Union-Find data structure
S2. Algorithm analysis
S3: Data structures: stacks, queues, lists, bags
S4: Elementary sorting: selectionsort, insertionsort, shellsort
S5: Advanced sorting: mergesort, quicksort, heapsort
S6. Complexity of sorting problems
S7: Priority Queues
S8. Elementary symbol tables
S9. Binary search trees
S10. Balanced search trees
S11. Hash tables

Period 1: Assessment throughout the semester or Final Exam
Assessment throughout the semester, requiring attendance at least 3/4 of the classes:
- 2 practical tests (60%), with a minimum grade of 7.5 in each.
- 2 theoretical tests (40%), with a minimum grade of 7.5 in each.
The final weighted average between the theoretical and practical tests must be equal to or higher than 9.5.
Assessment by Exam:
- (100%) Final Exam with theoretical and practical components
Students have access to the assessment by Exam in Period 1 if they choose it at the beginning of the semester or if they fail the assessment throughout the semester.
Period 2: Final Exam
- (100%) Final Exam with theoretical and practical components
Special Period: Final Exam
- (100%) Final Exam with theoretical and practical components

António Jorge Filipe Fonseca

LO1: Identify data types and the main steps in the data analysis process.
LO2: Organize and prepare data in spreadsheets.
LO3: Understand and use the main concepts of descriptive statistics, choosing appropriate measures and graphical representations to describe the data.
LO4: Explain what a data model is and understand some predictive data modeling techniques.
LO5: Create effective visualizations with pivot tables and graphs.
LO6: Interpret results and present conclusions clearly and reasonedly.

CP1: Introduction to data analysis – types, sources, and data lifecycle.
CP2: Organizing and cleaning data in Excel – formats, filters, validation. Basic and advanced Excel functions – statistics, logic, search, and reference.
CP3: Basics of Descriptive Statistics: Types of variables. Frequency tables and graphical representations. Measures of central tendency, dispersion, skewness, and kurtosis. Exploratory data analysis.
CP4: Basics of predictive data modeling: Simple and multiple linear regression, logistic regression.
CP5: Data visualization – graphs, conditional formatting, simple dashboards.

Assessment throughout the semester includes:

- Practical data analysis project completed in a group (maximum 3 students), including a report (15%), with mandatory midterm submission (15%) and oral defense with individual grading (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- In-class exercises and weekly assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

LO1: Understand the main theories, concepts, and issues related to Work, Organizations, and Technology;
LO2: Understand the main processes of the digital transition directly related to the world of work and its organizations;
LO3: Analyze the multiple social, economic, and political implications brought by the digital transition;
LO4: Explore cases, strategies, and application methods to understand the real impacts of the digital transition on professions, companies, and organizations.

PC1. Is work different today than it was in the past?
PC2. What rights and duties in the world of work?
PC3. How has theory looked at technology?
PC4. What digital technologies are changing work?
PC5. What future for work?
PC6. Is artificial intelligence really that intelligent?
PC7. Where does precariousness begin and end?
PC8. Who is to blame when the machine makes a mistake?
PC9. Do digital technologies change the relationship between unions and companies?
PC10. What digital transformation in Portugal?

Continuous assessment throughout the semester:
Each student will conduct a Flipped Classroom session, which represents 20% of the final grade.

Individual work accounting for 35% of the final grade.

Group work accounting for a total of 35% of the final grade (10% for the group presentation and 25% for the written work).

Attendance and participation in classes represent 10% of the final grade. A minimum attendance of no less than 2/3 of the classes is required.

Each assessment element must have a minimum grade of 8. The final average of the various elements must be equal to or greater than 9.5.

Examination evaluation (1st Period if chosen by the student, 2nd Period, and Special Period): in-person exam representing 100% of the final grade with a minimum grade of 9.5.

António Raimundo

Ricardo Teixeira da Costa Pereira

At the end of the course unit, students should:
LO1: Recognize the advantages and challenges of using Artificial Intelligence (AI) techniques and approaches, demonstrating critical awareness of informed and uninformed search methods.
LO2: Select and justify the most appropriate technological approaches and algorithms, including search methods, representation, and reasoning logics.
LO3: Apply the concepts and techniques discussed in the design and development of AI-based systems, as well as in the modeling of examples based on real scenarios.
LO4: Develop, implement, and evaluate solutions involving predicate logic and logic programming.
LO5: Understand the fundamentals of fuzzy logic and be able to implement fuzzy logic-based solutions to solve specific problems.
LO6: Work autonomously and in groups to develop projects that apply the acquired knowledge, demonstrating the ability to adapt and solve complex problems in the AI field.

S1: Fundamental notions of AI with emphasis on the search-based approach.
S2: Search algorithms: depth first and breadth first, A*, greedy BFS, Dijkstra.
S3: Fundamental notions relating to knowledge, representation and the architecture of knowledge-based systems.
S4: First-order predicate logic: representation and deduction.
S5: Declarative knowledge represented in Logic Programming.
S6: Fuzzy logic.

The assessment throughout the semester (ALS) consists of three assessment blocks (AB) and each AB consists of one or more assessment moments. This structure follows the following distribution:

- AB1: 2 practical mini-tests [20% each practical mini-test * 2 = 40%]
- AB2: 2 theoretical mini-tests [15% each theoretical mini-test * 2 = 30%]
- AB3: 1 final project [30%]

Exam assessment:
- 1st Epoch [100%]
- 2nd Epoch [100%]

The practical mini-tests, taken individually in class, focus on problem solving, involving the implementation, analysis, and validation of algorithms.

The theoretical mini-tests, also taken individually in class, aim to assess the theoretical knowledge underlying the syllabus content.

The project consists of a group deliverable, including individual oral discussions, which allows for the assessment of content consolidation and problem-solving skills.

In any AB, an individual oral discussion may be required to confirm authorship and/or assess knowledge mastery at any time during the semester.

All periodic assessment blocks (AB1, AB2, and AB3) have a minimum grade of 8.5.

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the CU and/or assignment of 0 points to the assessment element.

To pass the course unit, students must achieve a minimum final grade of 10, resulting from the weighted sum of all assessment elements.

The exam assessment consists of a written exam covering all the knowledge included in the course unit syllabus and has a weighting of 100%.

Class attendance is not mandatory.

By the end of this course unit, the student should be able to:
LO1: Apply fundamental programming concepts.
LO2: Create procedures and functions with parameters.
LO3: Understanding the syntax of the Python programming language.
LO4: Develop programming solutions for problems of intermediate complexity.
LO5: Explain, execute and debug code fragments developed in Python.
LO6: Interpret the results obtained from executing code developed in Python.
LO7: Develop programming projects.

PC1. Integrated development environments. Introduction to programming: Logical sequence and instructions, Data input and output.
PC2. Constants, variables and data types. Logical, arithmetic and relational operations.
PC3. Control structures.
PC4. Lists and Lists of Lists
PC5. Procedures and functions. References and parameters.
PC6. Objects and object classes.
PC7. File Manipulation.

This course follows a semester-long project-based assessment model due to its predominantly practical nature, and does not include a final exam.

Students are assessed based on the following components (A1 + A2):
A1: Learning Tasks with teacher validation (30%)
Five learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades for the five tasks. To pass A1, the student must meet one of the following requirements:
- obtain at least 7 points in each of the five tasks
or
- obtain a minimum average of 8 points across the five tasks.

A2: Mandatory Group Project (3) with theoretical-practical discussion (70%)
Minimum grade of 9.5 points.
Late submissions will result in penalties.

Remediation:
Students who do not achieve the minimum overall grade may complete an individual Practical Project (100%) with oral discussion.
If a student misses an exam due to absence, or does not achieve the minimum grade of 7 points, they may take a make-up exam at the end of the semester.

Attendance:
A minimum attendance of 2/3 of classes is required.

The objective of the UC is to develop a technological project in line with the scope of the Course. Contact will be established with project planning considering the main phases: Requirements analysis, development, partial tests and final tests and changes. Contact with laboratory equipment and tools is one of the goals for designing a software, hardware or both project.

I. Introduction to technological innovation along the lines of Europe
II. Planning a technological project and its phases
III. Essential aspects for the development of a project
IV. Definition of material resources
V. Budget of a project
VI. Partial and joint Test Plan
VII. Presentation of a technological project
VIII. Technological project demonstration
IX. Preparation of Technical Report

Periodic grading system:
- Group project: first presentation: 30%; second presentation and exibithion: 40%; final report: 30%. The presentations, demonstrations and defence are in group.

LG1. Review the concept of function and its properties. Types of functions and operations with functions.
LG2. Graphics of elementar functions and function transformations.
LG3. Limits, indeterminations and graphic interpretation. Continuity.
LG4. Derivatives and its applications. Graphic interpretation.
LG5. Linear approximations and higher order approximations.
LG6. Derivative of composed functions and inverse functions.
LG7. Calculations with matrices and vectors.
LG8. Calculating detrminants and applicating its proprieties.
LG9. Knowing the concept of linear transformation and representation with matrices.
LG10. Calculating eigenvalues and eigenvectors.

PC1. Function. Elementar functions, Different type of functions. Operations with functions. Logaritmic and trigonometric functions.
PC2. Limits of a function at a point, Continuity at a point. Assimptotic lines.
PC3. Derivative of a function at a point. Derivative rules. Optimization problems.
PC4. Derivative of composed functions – chain rule. Derivative of the inverse function.
PC5. Linear approximation and Taylor approximation.
PC6. Solving linear equation systems. Matrices and operations. Inverting matrices. Determinants and properties. Linear transformations.
PC7. Real vector space. Inner product. Parallelism and perpendicularity.
PC8. Eigenvalues, eigenvectores and matrix diagonalization.

Passing with a grade not lower than 10 points in one of the following modalities:
- Assessment throughout the Semester:
* 7 assignments/mini-tests conducted during classes. The best 5 are counted, each with a weight of 5% (total of 25%).
* autonomous work, with a weight of 5%.
* applied mathematics project, with a weight of 10%.
* Final test to be conducted on the date of the first exam period, with a weight of 60% and a minimum grade of 8 points
or
- Examination Assessment (100%).

There is the possibility of conducting oral examinations.
Grades above 17 points must be defended orally.

LG1 Dominate the concepts of sequence and numerical series
LG2 Calculate limits of sequences and, relative to a series, find out the existence of sum
LG3 Understand the generalization of the concept of series to functional series and obtain the convergence domain
LG4 Understand the definition of integral as the limit of Riemann sums
LG5 Calculate primitives and apply them to determine the value of integrals
LG6 Apply integrals to calculate areas, lengths and mean values
LG7 Solve 1st order linear ordinary differential equations (ODEs)
LG8 Calculate partial derivatives and directional derivative
LG9 Interpret the gradient vector as the direction of maximal increase of a function
LG10 Decide about the existence of a tangent plane
LG11 Obtain the 1st order Taylor development and, explore numerically in higher order
LG12 Obtain unconstrained extrema(otimization)

PC1 Sequences. Monotony. Bounded sequences. Geometric progression
PC2 Convergence of sequences
PC3 Numerical series, partial sums and sum
PC4 Convergence criteria of series of non-negative terms
PC5 Simple and absolute convergence of alternating series. Leibniz's criterion
PC6 Power series and domain of convergence
PC7 Antiderivatives. Riemann definite integral. Fundamental theorem of calculus
PC8 Integration by parts and change of variables. Decomposition into simple fractions
PC9 Applications of integral (area, length, mean value)
PC10 Improper integral and convergence
PC11 First order linear ODE
PC12 Multivariable real functions. Level curves. Limits and continuity
PC13 Partial derivatives at a point and gradient vector. Linear approximation, tangent plane and differentiability
PC14 Directional derivative. Chain rule. Taylor's polynomials and series
PC15 Quadratic forms and otimization problems

Approval with a grade >= 10 points (out of 0 to 20) in one of the following modalities:
- Assessment throughout the semester:
* 4 mini-tests conducted during classes, each with a weight of 5% (total of 20%).
* Mid-semester test (15%).
* Autonomous work (5%).
* Final test (60%) on the date of the first exam period, with a minimum grade of 8 points.

Or

- Assessment by exam (100%).

There is the possibility of conducting oral exams.
Grades above 17 points must be defended orally.

Maria de Fátima Alves de Pina

By the end of the course, students should have acquired the following skills:
LO1 - understand the fundamental concepts of inferential statistics;
LO2 - calculate the size of a sample;
LO3 - estimate and interpret a confidence interval;
LO4 - choose the test to use in each situation;
LO5 - apply and interpret statistical tests;
LO6 - carry out the chosen test using statistical software;
LO7 - know how to report statistical results in a report;
LO8 - analyze bivariate data.

S1. Basic concepts of descriptive statistics, inferential statistics, probabilities and normal distribution
S2. Estimation
S2.1 Confidence intervals for the mean and proportion
S2.2 Determining sample size
S3. Hypothesis tests: parametric and non-parametric tests
S3.1 One-sample t-test
S3.2 t/Mann-Whitney test for two independent samples
S3.3 One-factor ANOVA/Kruskal-Wallis
S3.4 Chi-squared test of independence
S4: Correlation and Regression Line

Pass with a mark of not less than 10 in one of the following ways:
- Assessment throughout the semester: 1 mini-test taken in class (20%) + Final written test taken on the date of the 1st period (60%) + group project (20%),
All assessment elements are compulsory and have a minimum mark of 8.
or
- Assessment by Exam (100%).

Notes:
1) A complementary oral assessment may be conducted after any evaluation moment to validate the final grade.
2) For this Course Unit, students must consult the Iscte Academic Code of Conduct, available on the institutional platforms, to ensure compliance with the established ethical and behavioral standards.

LO1. Acquiring knowledge about the fundamentals and stages of the Design Thinking process
LO2. Develop skills such as critical thinking, collaboration, empathy and creativity.
LO3. To apply Design Thinking in problem solving in several areas, promoting innovation and continuous improvement.

S1. Introduction to Design Thinking and Stage 1: Empathy (3h)
S2. Steps 2 and 3: Problem Definition and Ideation (3h)
S3. Step 4: Prototyping (3h)
S4. Step 5: Testing and application of Design Thinking in different areas (3h)

Semester-long Assessment Mode:

• Class participation (20%): Evaluates students' presence, involvement, and contribution in class discussions and activities.

• Individual work (40%): Students will develop an individual project applying Design Thinking to solve a specific problem. They will be evaluated on the application of the stages of Design Thinking, the quality of the proposed solutions, and creativity.

• Group work (40%): Students will form groups to develop a joint project, applying Design Thinking to solve a real challenge. Evaluation will be based on the application of the steps of Design Thinking, the quality of the solutions, and collaboration among group members.

To complete the course in the Semester-long Assessment mode, the student must attend at least 75% of the classes and must not score less than 7 marks in any of the assessment components. The strong focus on learning through practical and project activities means that this course does not include a final assessment mode.

LO1. Develop specific oral communication skills for public presentations.
LO2. Know and identify strategies for effective use of the vocal apparatus.
LO3. Identify and improve body expression. LO4. Learn performance techniques.
The learning objectives will be achieved through practical and reflective activities, supported by an active and participatory teaching method that emphasizes experiential learning. The knowledge acquired involves both theatrical theory and specific oral communication techniques. Students will learn about the fundamentals of vocal expression, character interpretation and improvisation, adapting this knowledge to the context of public performances.

PC1. Preparing for a presentation.
PC2. Non-verbal communication techniques.
PC3. Voice and body communication, audience involvement. PC4. Presentation practice and feedback. The learning objectives will be achieved through practical and reflective activities, supported by the active and participatory teaching method which emphasizes experiential learning. Classes will consist of activities such as: Theatrical experiences and group discussions; Practical activities; Presentations and exhibitions of autonomous work; Individual reflection.

The assessment of the Public Presentations with Theatrical Techniques course aims to gauge the development of students' skills in essential aspects of public presentations. The assessment structure includes activities covering different aspects of the experiential learning process involving both theatrical techniques and specific communication techniques.
Assessment throughout the semester includes activities covering different aspects of the process of preparing a public presentation, including group and individual work activities:
Group activities (50%) [students are challenged to perform in groups of up to 5 elements, made up randomly according to each activity proposal].
1-Practical Presentations: Students will be assessed on the basis of their public presentations throughout the semester:
Description: each group receives a presentation proposal and must identify the elements of the activity and act in accordance with the objective.
The results of their work are presented in class to their colleagues (Time/group: presentation - 5 to 10 min.; reflection - 5 min.). Assessment (oral): based on active participation, organization of ideas and objectivity in communication, vocal and body expression, the use of theatrical techniques and performance. Presentations may be individual or group, depending on the proposed activities.
Individual activities (50%)
1-Exercises and Written Assignments (Autonomous Work):
Description: In addition to the practical presentations, students will be asked to carry out exercises and written tasks related to the content covered in each class. These activities include reflecting on techniques learned, creating a vision board, analyzing academic objectives, student self-assessment throughout the semester, answering theoretical questions and writing presentation scripts.
Assessment: (Oral component and written content), organization, content, correct use of the structure and procedures of the autonomous work proposed in each class, ability to answer questions posed by colleagues and the teacher. Communication skills and the quality of written work will be assessed, with a focus on clarity of presentation. These activities will help to gauge conceptual understanding of the content taught.
There will be no assessment by final exam, and approval will be determined by the weighted average of the assessments throughout the semester.
General considerations: in the assessment, students will be given feedback on their performance in each activity.
To complete the course in this mode, the student must attend 80% of the classes. The student must have more than 7 (seven) points in each of the assessments to be able to remain in evaluation in the course of the semester.

OA1 - To be trained in the ethical and responsible use of Generative Artificial Intelligence (AI) tools
OA2 - To acquire critical analysis skills on the results produced by Generative AI tools
OA3 - To be able to identify and develop creative solutions in solving ethically and socially complex problems with Generative AI
OA4 - To be able to apply Generative AI tools in the preparation of academic work, in particular in the application of academic writing and in the use of normative citation and referencing procedures.

CP1 - Introduction to AI and Generative AI:
* Theoretical exposition on the historical context, evolution and important concepts about Artificial Intelligence (AI) and Generative AI
CP2 - Prompt Engineering:
* Explanation of good practices for interacting with generative language models
CP3 - Generative AI Tools:
* Exploration of multiple Generative AI tools, based on text, images and videos
CP4 - Formation of argumentative content:
* Development of creative solutions using argumentation practices and Generative AI tools
CP5 - Rules for scientific writing:
* Application of citation and referencing standards (APA standards) in academic writing

The Semester-Long Assessment includes the following activities:
1. Individual Activities (50%)
1.1 Prompt Simulations with AI Tools in an Academic Context (20%):
* Description: The student must create a clear/justified, well-structured prompt, according to the script proposed by the instructor in class.
* Assessment: (submit in Moodle), communication and teamwork skills based on the quality of the prompt simulations performed.
1.2 Oral Defense - Group Presentation - 5 min. Discussion - 5 min. (30%):
* Description: Each student must present their contributions to the work completed to the class.
* Assessment: After the student's presentation, there will be a question-and-answer session.
2. Group Activities (50%) [students are organized into groups of up to 5 students, randomly selected], which include:
* Group presentations, reviews, edits, and validations of AI-generated content. The assessment (to be submitted in Moodle) includes gathering relevant information, assessing the clarity and innovative nature of the use of structured prompts.
* Development of strategies for reviewing, editing, and validating AI-generated content. Students will be asked to critically evaluate and reflect on the ethical challenges of integrating AI into an academic environment. The assessment (to be submitted in Moodle) will consist of correcting the work based on the accuracy and quality of the reviews and edits, as well as student participation in providing feedback to their peers.
* Final Project Presentation Simulations, where groups choose a topic and create a fictitious project following the structure of a technical report or scientific text. They present their project in class (5 min.) and discuss the topic (5 min.). The assessment (to be submitted in Moodle) will consider the organization, content, correct use of the structure, and procedures of the academic work.

General Considerations:
Feedback on student performance in each activity will be provided during the Semester Assessment.
To be assessed throughout the semester, students must attend 80% of classes and achieve a score of at least 7 points in each assessment.
If there are questions about participation in the activities, the instructor may request an oral discussion.
The group must ensure that at least one computer is available for each group to allow for classroom activities.
There will be no final exam assessment; passing will be determined by the weighted average of the assessments throughout the semester. Assessments in the second and special assessment periods will have an alternative assessment method, so any students wishing to take the assessment in these assessment periods should contact their instructor in advance to learn about the assessment procedure.

LO1: To understand Web technologies, programming languages for Web, and usual architectures
LO2: Identify and explain different types of cloud architecture and their key features;
LO3: Identify and use the key technologies enabling cloud computing;
LO4: Propose appropriate cloud architectures for a particular application;

SY1: Standard W3C and Web programming
SY2: Client-Server architectures
SY3: Model View Controler (MVC)
SY4: Cloud Principles and Business Drivers;
SY5: Pre-Cloud technology, Virtualisation, Hypervisors, Xen, Virtual Clusters;
SY6: XaaS, Public, Private, Hybrid Clouds, Examples;
SY7: Basics on the development of Cloud applications;

This Unit is accomplished through 2 projects that worth 50% each:
(1) Development of a Web Application
(2) Development of a cloud-based project
In the first evaluation period, the project is developed in groups of students with an individual discussion (that accounts for 50% of the grade in each project). As for the second and extra evaluation period, the projects are developed individuallyl.

"By the end of this course unit, the student should be able to:
LO1: Understand fundamental concepts and representation of graphs.
LO2: Analyse paths and perform searches in networks.
LO3: Calculate centrality measures and analyse network structures.
LO4: Apply distributions and algorithms to complex networks.
LO5: Model networks and adapt empirical data to network models.
LO6: Comprehend the fundamentals of neural networks.
LO7: Use computational tools for network representation and visualisation."

"PC1. Fundamentals of Graphs and Networks
1.1. Introduction to Graph Theory
1.2. Mathematics of Graphs
1.3. Search Algorithms
PC2. Structure and Dynamics of Complex Networks
2.1. Metrics and Centrality Measures
2.2. Network Models
2.3. Advanced Topics
PC3. Applications and Visualisation of Networks
3.1. Adapting Data to Network Models
3.2. Applications in Various Fields
3.3. Network Visualisation with Computational Tools
PC4. Introduction to Neural Networks
4.1. Perceptron model and basic fundamentals
4.2. Neural network architecture
4.3. Training and optimization"

"Approval requires a grade of no less than 10 out of 20 in one of the following modalities:
- Assessment throughout the semester: 1 group project (40% - presentation on the date of the 1st exam period) + 2 midterm tests (25% each) + autonomous work activities (10%); all assessment components require a minimum grade of 8 out of 20.
- Assessment by Exam (100%), in any of the exam periods.
- A complementary oral assessment may be conducted after any assessment moment to validate the final grade."

LO1. Understand the main methods of non-supervised learning.
LO2: Evaluate, validate, and interpret the results of non-supervised models.
LO3: Develop knowledge discovery projects from data using non-supervised learning models.
LO4: Acknowledge, through the approach of various problem contexts (e.g., customer segmentation) in which unsupervised learning can effectively provide solutions relevant to these problems.
LO5. Understand the theoretical and practical foundations of reinforced learning.
LO6. Implement and test reinforced learning algorithms in simulated environments to understand the dynamics between actions and consequent rewards.
LO7. Evaluate and optimize the performance of reinforced learning models using appropriate metrics.
LO8. Learn and apply unsupervised and reinforced algorithms in practical case studies.

SY1: Introduction to unsupervised learning: fundamental concepts, types of algorithms and practical applications.
SY2: Dimensionality reduction and data visualization: Principal Component Analysis (PCA), t-SNE and UMAP for dimensionality reduction and visual interpretation.
SY3: Clustering and segmentation techniques: exploration of algorithms such as K-Means, DBSCAN, Expectation-Maximization (EM), hierarchical clustering.
SY4: Outlier analysis and detection using unsupervised techniques: KNN, LOF, iForest.
SY5: Association rules and the Apriori algorithm.
SY6: Self-Organizing Maps (SOMs): application of self-organizing maps for visualization and analysis of complex patterns in large volumes of data.
SY7: Reinforcement learning techniques: Q-Learning, SARSA. Introduction to concepts and practical implementation.
SY8: Exploration vs. exploitation in reinforcement learning: strategies for balancing decision-making.

As this course is of a very practical and applied nature, it follows the 100% project-based assessment model throughout the semester, which is why this course does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. It is organized as follows:

- AB1: 1st tutorial + 1st mini-test [20% for the 1st tutorial + 10% for the 1st mini-test = 30%]
- AB2: 2nd tutorial + 2nd mini-test [20% for the 2nd tutorial + 10% for the 2nd mini-test = 30%]
- AB3: 1 final project [40%]

All periodic assessment blocks (AB1, AB2 and AB3) have a minimum mark of 8.5. In any AB, it may be necessary to hold an individual oral discussion to assess knowledge.

The tutorials consist of individual oral discussions to assess the students' performance in the projects proposed for the tutorial.

Mini-tests are used to assess the theoretical knowledge applied to each of the projects also assessed during tutorials.

The final project consists of the development of a practical piece of work that brings together the knowledge and skills acquired throughout the semester, in which external organizations / companies may participate in the proposed challenge.

The 1st Season and 2nd Season can be used for assessment.
Attendance at classes is not compulsory.

LO1. Know the history of machine learning; know and understand the different types of machine learning: concepts, foundations and applications.
LO2. Know the concepts that enable Exploratory Data Analysis (EDA) to be carried out, as well as understanding its importance in problem-solving and decision-making.
LO3. Learn Data Wrangling mechanisms - preparing data for input to a supervised algorithm.
LO4. Know how to use continuous and categorical variables; distinguish between classification and regression
LO5. Know and analyze the results by applying performance evaluation metrics
LO6. Understand supervised algorithms: decision trees, linear and logistic regression, SVMs, Naive-Bayes and k-NN.
LO7. Understand ensemble algorithms: bagging and boosting
LO8. Know and understand hyperparameter optimization

S1. Introduction to Machine Learning: The history, foundations and basic concepts
S2. Exploratory Data Analysis (EDA): Data Wrangling and Data Visualization
S3. Classification and Regression; Continuous and categorical / discrete variables; performance evaluation metrics
S4. Supervised Learning: SVM, Decision Trees, Linear and Logistic Regression, Naive-Bayes and k-NN.
S5. Bagging and Boosting in supervised algorithms
S6. Hyperparameter optimization

This course unit (CU), due to its highly practical and applied nature, follows a 100% project-based continuous assessment model throughout the semester; therefore, it does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB includes one or more assessment moments. The distribution is as follows:

- AB1: 1st tutorial + 1st mini-test [20% for the 1st tutorial + 10% for the 1st mini-test = 30%]
- AB2: 2nd tutorial + 2nd mini-test [20% for the 2nd tutorial + 10% for the 2nd mini-test = 30%]
- AB3: 3rd tutorial + 3rd mini-test [30% for the 3rd tutorial + 10% for the 3rd mini-test = 40%]

Tutorials consist of projects to be developed in groups, which also include individual oral discussions, allowing for the assessment of students' performance in these same projects.

Mini-tests, taken individually, allow for the assessment of theoretical knowledge applied to each of the projects evaluated in tutorials.

In any AB, there may be a need to conduct an individual oral discussion to assess knowledge.

All periodic assessment blocks (AB1, AB2, and AB3) have a minimum grade of 8.5.

The student's physical presence is mandatory in all assessment elements, under penalty of failure in the curricular unit and/or assignment of 0 points to the assessment element.

This CU may include the participation of external organizations or companies, which may propose projects that are properly framed within the curriculum and aligned with the learning objectives of the CU.

The 1st and 2nd Epochs may be used for assessment.

Class attendance is not mandatory.

At the end of the learning unit, the student must be able to: LG.1. Understand entrepreneurship; LG.2. Create new innovative ideas, using ideation techniques and design thinking; LG.3. Create value propositions, business models, and business plans; LG.5. Develop, test and demonstrate technology-based products, processes and services; LG.6. Analyse business scalability; LG.7. Prepare internationalization and commercialization plans; LG.8. Search and analyse funding sources

I. Introduction to Entrepreneurship;
II. Generation and discussion of business ideas;
III. Value Proposition Design;
IV. Business Ideas Communication;
V. Business Models Creation;
VI. Business Plans Generation;
VII. Minimum viable product (products, processes and services) test and evaluation;
VIII. Scalability analysis;
IX. Internationalization and commercialization;
X. Funding sources

Periodic grading system: - Group project: first presentation: 30%; second presentation: 30%; final report: 40%.

At the end of this course, the student should be able to:
LO1. Understand cybersecurity in its different perspectives
LO2. Understand the main security challenges and threats that organisations and users have to face;
LO3. Introduce the legal, ethical and strategic context of information security
LO4. Identify and manage information security risk;
LO5. Know and apply appropriate security technologies for risk mitigation;
LO6. Know mechanisms for the management and maintenance of information security environments.

SC1. Introduction to Cybersecurity: main components; cybersecurity pillars; cybersecurity frameworks.
SC2. Information Security Planning and Legal and Ethical Framework
SC3. Principles of Information Security Governance and Risk Management
SC4. Introduction to Information Security Technology: access controls, firewalls, vpns, idps, cryptography and other techniques.
SC5. Physical Security: physical access control mechanisms, physical security planning, among others.
SC6. Information Security Implementation: information security project management; technical and non-technical aspects of information security implementation.
SC7. Personnel Security: personnel security considerations; personnel security practices.
SC8. Maintenance of Information Security.

Assessment throughout the semester:
- Carrying out a set of group projects and activities (60%) throughout the semester.
- Two individual tests (40%) [minimum mark of 6 for each test].
Attendance at a minimum number of classes is not compulsory for the assessment throughout the semester.
Assessment by exam:
For students who opt for this process or for those who fail the periodic assessment process, with 3 seasons under the terms of the RGACC.

At the end of the UC, students will be able to:
(LO1) Understand, define and describe what autonomous agents are and how they differ from traditional software.
(LO2) Understand the different agent architectures and their applications.
(LO3) Describe how agents perceive and represent their environment, reason under uncertainty and how they represent knowledge.
(LO4) Explain key concepts such as states, actions, rewards and policies in decision-making contexts.
(LO5) Apply search algorithms to solve problems in an agent context.
(LO6) Implement planning algorithms for agents in complex environments.
(LO7) Apply basic reinforcement learning techniques (such as Q-learning) to simple problems.
(LO8) Evaluate the performance and behavior of agents.
(LO9) Understand and describe what multi-agent systems are.
(LO10) Discuss ethical, security and social considerations related to autonomous agents.

(PC1) What are agents and environments? Agent architectures (reactive, deliberative, hybrid).
(PC2) Perceptions/Sensors, types of environments (fully/partially observable, deterministic/stochastic).
(PC3) Demand (BFS, DFS, UCS) goal-based agents, informed demand, heuristic functions and admissibility, constraint satisfaction problems.
(PC4) States, actions, transitions, rewards, policies; introduction to MDPs, classic partial order and hierarchical planning problems in dynamic environments.
(PC5) Reinforcement Learning (AR): Basic AR algorithms, value iteration, Q-learning, action exploration.
(PC6) Multi-agent systems: cooperation and competition, negotiation and coordination, self-organization, emergence and communication.
(PC7) Agent-based modeling and simulation (ABMS) and complex adaptive systems.
(PC8) Ethical and social issues in the construction of agents.

Assessment in the 'throughout the semester' mode results from an individual interim test in Moodle (20% of the final grade), group work (maximum of 3 students) with the preparation of a report (20% of the final grade) and an oral presentation (20% of the final grade) to be made by the group and the completion of 4 homework assignments (10% of the final grade each)
A minimum attendance of no less than 2/3 of classes is required (students can miss 4 classes out of 12).
The Final Exam is a written, individual, open-book exam covering all the material. Those who did not successfully complete the assessment throughout the semester, with an average grade greater than or equal to 10 (out of 20), take the final exam, in the 1st season, 2nd season or special season.

LO 1 Explain what databases and information systems are, characterising them in terms of both technology and their importance to organisations.
LO 2. formally represent information requirements by drawing up conceptual data models.
LO 3 Explain the Relational Model and data normalisation, highlighting their advantages and the situations in which they should be applied.
LO 4 Design relational databases that respond to requirements specified by conceptual data models.
LO 5. build and programme a relational database using the SQL language.
LO 6. manipulate data - i.e. insert, query, alter and delete - using the SQL language.
LO 7. Explain what database administration consists of, why it is necessary and how its most essential tasks are carried out.

S1. Introduction to Information Systems and their role in organisations.
S2. Introduction to Information Systems Analysis with UML: Introduction, requirements analysis, data models, schemas and UML diagrams.
S3. Database Design. Relational Model: relationships, attributes, primary keys, foreign keys, integrity rules, normalisation and optimisations.
S4. SQL Language. Tables, relational algebra, simple queries, subqueries, operators (SELECT, Insert, delete, update), views, indexes, triggers, stored procedures and transactions.
S5. Administration and Security in Database Management Systems (DBMS).

Assessment throughout the semester:

- 3 tests to be taken during the semester (70%). Minimum grade of 8 points for each tests.
- 1 modelling and implementation project (in groups of up to 3 people) (30%). The minimum project grade is 10 points. Completion of the project is mandatory for approval. The project is assessed in groups with individual oral discussion.

Assessment by exam:
- One exam, weighted 100%

The minimum passing grade for the course unit is 10 out of 20.
Attendance at 2/3 of the scheduled classes is mandatory for approval.

At the end of this UC, the student should be able to:
LG.1. Present the image of the product/service in a website
OA.2. Present the image of the product/service in social networks
OA.3. Describe functionalities of the product/service
OA.4. Describe phases of the development plan
OA.5. Develop a prototype
OA.6. Test the prototype in laboratory
OA.7. Correct the product/service according to tests
OA.8. Optimize the product/service considering economic, social, and environmental aspects
OA.9. Adjust the business plan after development and tests, including commercialization and image
OA.10. Define product/service management and maintenance plan

I. Development of the product/service image
II. Functionalities of the product/service
III. Development plan
IV. Development of the product/service (web/mobile or other)
V. Revision of the business plan
VI. Management and maintenance of the product/service
VII. Certification plan
VIII. Intellectual property, patents, and support documentation
IX. Main aspects for the creation of a startup - juridical, account, registry, contracts, social capital, obligations, taxes

Periodic grading system:
- Group project: first presentation: 30%; second presentation: 30%; final report: 40%. The presentations, demonstrations and Defence are in group.

LO1 Structure the students' logical thinking in order to solve programming problems.
LO2 Empower students with the ability to perceive the object-oriented programming paradigm.
LO3 Use an object-oriented programming language and the necessary tools, to design, develop, test and debug small applications.
LO4 Understand and apply the concepts of abstraction, encapsulation, inheritance and polymorphism.
LO5 Know how to use the fundamental data structures of a library (stacks, queues, trees, scatter tables).
LO6 Apply error control mechanisms.
LO7 Explain the usefulness of using software design patterns and demonstrate their use in simple cases.
LO8 Develop creativity, technological innovation and critical thinking.
LO9 Develop self-learning, peer review, teamwork, and oral expression.

S1 Classes and Objects
S2 Inheritance and polymorphism
S3 Abstract classes
S4 Interfaces and comparators
S5 Collections: lists, sets, maps
S6 Anonymous classes and lambdas
S7 Reading and writing files
S8 Exceptions and error handling
S9 Genericity and design patterns
S10 JUnit Tests and Documentation

CU with Periodic Assessment, not including a Final Exam:
8 individual assignments (10%), min grade of 9.5
Group project, with oral discussion (45%), min grade of 9.5
2 Mini-tests (45%), minimum grade of 9.5
If failing in the first period (< 10 out of 20), the student can retake the 1st and/or 2nd mini-tests (can also retake if scoring below the minimum grade in either or both) - accounting for 55% of the grade, with passing the Project or completing an individual project being mandatory - 45%

LO1: Explain the concepts and components of analytical information systems and Business Intelligence.
LO2: Model and transform data using Power BI.
LO3: Create interactive reports and dashboards tailored to different management needs.
LO4: Analyze and interpret data to support organizational decision-making.
LO5: Evaluate the quality of information and the effectiveness of analytical visualizations.

CP1: Introduction to Business Intelligence – concepts, architecture, and tools.
CP2: Data integration and transformation – Power Query, M, connections to multiple sources.
CP3: Data modeling – relational tables, DAX, measures, and KPIs.
CP4: Data visualization – creating reports, dashboards, storytelling with data.
CP5: Assessment and best practices in BI projects – data ethics, governance, information quality.

Assessment throughout the semester includes:

- Practical project for developing an analytical information system carried out in a group (maximum of 3 students), including a report (15%) with a mandatory midterm submission (15%) and an oral defense with an individual grade (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- Weekly in-class and independent assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all the material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

By the end of this CU, students should be able to:
LO1: Identify classes of problems that benefit from the use of advanced search algorithms.
LO2: Apply advanced search algorithms to real problems, demonstrating mastery of optimized strategies and techniques.
LO3: Select and justify appropriate heuristics according to the nature of each problem, considering complexity and available resources.
LO4: Analyze and evaluate different search approaches, considering performance, accuracy, and scalability.
LO5: Compare and differentiate genetic algorithms from other heuristic search strategies, evaluating the results obtained.
LO6: Plan and implement advanced search solutions in optimization scenarios, applying hybrid or combined strategies.

S1. Strategies with genetic algorithms: evolutionary theory and biological terminology.
S2. Evaluation functions and genetic operators (selection, crossover, mutation). Stopping criteria.
S3. Heuristics and problem representation; search spaces.
S4. Optimization tasks, satisfiability and semi-optimization.
S5. Basic heuristic search strategies: uninformed and informed search.
S6. Advanced heuristic search strategies: heuristics limited by memory and time.

Given the practical and applied nature of this course, the assessment model is followed throughout the semester (ALS) and does not include a final exam. The assessment consists of 2 assessment blocks (AB), and each AB consists of one or more assessment moments. It is distributed as follows:

- AB1: 1st project + 1st mini-test [20% for the 1st project + 15% for the 1st mini-test = 35%]
- AB2: 2nd project + 2nd mini-test [50% for the 2nd project + 15% for the 2nd mini-test = 65%]

Each assessment block (AB1 and AB2) requires a minimum mark of 8.5. In any AB, an individual oral discussion may be required to assess mastery of knowledge at any time during the semester.

The projects consist of deliverables developed in groups and individual oral discussions, allowing the consolidation of content and problem-solving skills to be assessed. The mini-tests aim to gauge the theoretical knowledge underlying the syllabus.

To pass the course, the student must achieve a minimum final mark of 10, resulting from the weighted sum of all the assessment elements.

A student is considered to have failed if they do not pass the ALS.

The 1st Epoch and 2nd Epoch may be used to carry out assessment moments, such as oral discussions and/or project presentations.

Attendance at classes is not compulsory.

At the end of the course unit, students should:
LO1. Apply AI project development methodologies to design innovative solutions aligned with economic, social, and environmental sustainability criteria.
LO2. Build empathy with users and their organizations, identify needs, obstacles, and opportunities, and formulate the problem and research questions to be addressed.
LO3. Conduct a systematic review of the literature, assessing the state of the art and best practices related to the problem.
LO4. Identify and specify functional and non-functional requirements, as well as identify the digital resources, data sets, and computational infrastructures needed for the project.
LO5. Plan, organize, and monitor the project life cycle, defining timelines and deliverables.
LO6. Work effectively in a team, participating in collaborative dynamics and communicating results clearly through technical reports and oral presentations.

S1. AI project-development and planning methodologies.
S2. Presentation of case studies and project topics in digital AI technologies (product, service or process).
S3. Selection of the project theme and its framing within the partner organisation
S4. Problem space: building user and organizational empathy; problem and research-question definition considering business requirements, user/client needs and technological challenges
S5. Application of a systematic literature review methodology and critical analysis of findings
S6. Identification of digital resources (including data collection), computational infrastructure and other assets required for the project.
S7. Preliminary implementation of proposed solutions within the selected case studies/projects; preparation of final reports and documentation; communication of results.

This course unit follows the assessment throughout the semester (ALS) by project at 100%, not including a final exam, given the adoption of the teaching method applied to real situations.

The process is based on a single evolutionary report divided into two deliverables:
- Preliminary Interim Report [40%]
- Final Preliminary Report [60%]

The Preliminary Interim Report includes the project proposal, the framework of the partner entity, the creation of empathy with users, the detailed definition of the problem, requirements, and objectives, as well as a systematic review of the literature and the planning of activities and resources. It includes an individual oral presentation and discussion.

The Final Preliminary Report contains a description of preliminary implementation, initial results/validation, critical analysis, and subsequent work plan. It is accompanied by a functional demonstration and individual public oral discussion.

Periodic meetings, in person and/or online, with the partner entity's mentor and the UC coordinating professor (or their appointee) are mandatory whenever requested. These sessions allow for progress to be validated, continuous feedback to be provided and, if necessary, the scope and planning to be adjusted, constituting a condition for the attribution of a grade to the deliverables.

The physical presence of the student is mandatory in all assessment elements, under penalty of failure in the UC and/or attribution of 0 points to the assessment element.

To pass the course unit, students must achieve a minimum final grade of 10, resulting from the weighted sum of all assessment elements.

The 1st and 2nd Epochs may be used for assessment.

Class attendance is not mandatory.

LO1: Define the main concepts, steps and methods involved in the development of Text Mining processes.
LO2: Atomize documents, create dictionaries, and perform other pre-processing tasks to prepare text for classification tasks.
LO3: Select and justify appropriate techniques for specific text processing tasks.
LO4: Construct vector representations from texts.
LO5: Explain the operation of algorithms for text classification, such as Naïve Bayes or KNN.
LO6: Apply a classifier to real cases.
LO7: Group documents using the k-means algorithm.
LO8: Develop prompt engineering in LLMs.

SY1: The utility of large amounts of text, challenges, and current methods.
SY2: Unstructured vs. (semi-)structured information.
SY3: Information retrieval and filtering, information extraction, and Data Mining.
SY4: Document preparation and cleaning, feature extraction, and term weighting strategies.
SY5: Vector space models and similarity measures.
SY6: Introduction to statistical machine learning and evaluation measures.
SY7: Supervised learning: Naïve Bayes, KNN, and k-means.
SY8: Sentiment analysis.
SY9: Resources for Text Mining.
SY10: Introduction to Deep Learning.
SY11: LLMs and Retrieval Augmented Generation (RAG) models.

This course follows the semester-long assessment model (ALS).

The ALS consists of the following elements:
- 1 practical work [40%]
- 3 mini-tests [20% each * 3 = 60% in total, with a minimum mark of 8.5]

The practical work can be done individually or in groups, consisting of the development of a project that will be subject to an individual oral discussion.

In case of failure in the ALS (<10 points), or if the student opts for Assessment by Exam, the exam corresponds to 100% of the grade.

After completing this UC, the student will be able to:

LO1. Identify the main themes and debates relating to the impact of digital technologies on contemporary societies;
LO2. Describe, explain and analyze these themes and debates in a reasoned manner;
LO3. Identify the implications of digital technological change in economic, social, cultural, environmental and scientific terms;
LO4. Predict some of the consequences and impacts on the social fabric resulting from the implementation of a digital technological solution;
LO5. Explore the boundaries between technological knowledge and knowledge of the social sciences;
LO6. Develop forms of interdisciplinary learning and critical thinking, debating with interlocutors from different scientific and social areas.

S1. The digital transformation as a new civilizational paradigm.
S2. The impact of digital technologies on the economy.
S3. The impacts of digital technologies on work.
S4. The impact of digital technologies on inequalities.
S5. The impacts of digital technologies on democracy.
S6. The impacts of digital technologies on art.
S7. The impacts of digital technologies on individual rights.
S8. The impacts of digital technologies on human relations.
S9. The impacts of digital technologies on the future of humanity.
S10. Responsible Artificial Intelligence.
S11. The impact of quantum computing on future technologies.
S12. The impact of digital technologies on geopolitics.

The assessment process includes the following elements:

A) Ongoing assessment throughout the semester
A1. Group debates on issues and problems related to each of the program contents. Each group will participate in three debates throughout the semester. The performance evaluation of each group per debate will account for 15% of each student's final grade within the group, resulting in a total of 3 x 15% = 45% of each student's final grade.
A2. Participation assessment accounting for 5% of each student's final grade.
A3. Final test covering part of the content from the group debates and part from the lectures given by the instructor, representing 50% of each student's final grade.
A minimum score of 9.5 out of 20 is required in each assessment and attendance at a minimum of 3/4 of the classes is mandatory.

B) Final exam assessment Individual written exam, representing 100% of the final grade.

"By the end of this course unit, the student should be able to:
LO1: Understand fundamental concepts and representation of graphs.
LO2: Analyse paths and perform searches in networks.
LO3: Calculate centrality measures and analyse network structures.
LO4: Apply distributions and algorithms to complex networks.
LO5: Model networks and adapt empirical data to network models.
LO6: Comprehend the fundamentals of neural networks.
LO7: Use computational tools for network representation and visualisation."

"PC1. Fundamentals of Graphs and Networks
1.1. Introduction to Graph Theory
1.2. Mathematics of Graphs
1.3. Search Algorithms
PC2. Structure and Dynamics of Complex Networks
2.1. Metrics and Centrality Measures
2.2. Network Models
2.3. Advanced Topics
PC3. Applications and Visualisation of Networks
3.1. Adapting Data to Network Models
3.2. Applications in Various Fields
3.3. Network Visualisation with Computational Tools
PC4. Introduction to Neural Networks
4.1. Perceptron model and basic fundamentals
4.2. Neural network architecture
4.3. Training and optimization"

"Approval requires a grade of no less than 10 out of 20 in one of the following modalities:
- Assessment throughout the semester: 1 group project (40% - presentation on the date of the 1st exam period) + 2 midterm tests (25% each) + autonomous work activities (10%); all assessment components require a minimum grade of 8 out of 20.
- Assessment by Exam (100%), in any of the exam periods.
- A complementary oral assessment may be conducted after any assessment moment to validate the final grade."

By the end of the course, students should be able to:
LO1. Understand and identify the problems associated with processing large amounts of information.
LO2. Understand the concepts and ecosystem of Big Data.
LO3. Know how to design and implement fault-tolerant data storage solutions in a distributed environment.
LO4. Know how to extract, manipulate and load large amounts of information from unstructured data sources.
LO5. Know how to manipulate and process non-relational databases.
LO6. Understand and apply distributed programming and computing models.
LO7. Understand and know how to apply techniques for handling JSON structures and real-time data flow.
LO8. Develop creativity, technological innovation and critical thinking.
LO9. Develop self-learning, peer review, teamwork, written and oral expression.

S1. The concept of Big Data, the applicable problems and the respective ecosystem.
S2. Introduction to non-relational databases and MongoDB.
S3. Computing architecture for Big Data: (1) redundant and fault-tolerant and (2) distributed to support large volumes of data. Example of the Hadoop platform and its distributed file system.
S4. The MapReduce programming model.
S5. Database design in MongoDB.
S6. Handling JSON structures and real-time data.
S7. The ETL (Extract, Transform and Load) process applied to datasets with denormalized real data and the development of Big Data processing applications in Spark and MongoDB environments.

Given the practical and applied nature of this course, the assessment model is followed throughout the semester (ALS) and does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. It is distributed as follows:

- AB1: 8 weekly assignments [2.5% * 8 = 20% in total]
- AB2: 2 mini-tests [15% each * 2 = 30% in total]
- AB3: Laboratory project [50%]

Each assessment block (AB1, AB2 and AB3) requires a minimum mark of 8.5. In any AB, an individual oral discussion may be required to assess knowledge at any time during the semester.

Weekly assignments allow students to respond to small weekly practical challenges related to the knowledge acquired. The mini-tests aim to assess the theoretical knowledge that underlies the syllabus. The laboratory project can be carried out individually or in groups, consisting of the preparation of a practical project which will then be the subject of an individual oral discussion.

To pass the course, the student must achieve a final mark of at least 10 points, resulting from the weighted sum of all the assessment elements.

A student is considered to have failed if they do not pass the ALS.

The 1st Epoch and 2nd Epoch may be used to carry out assessment moments, such as oral discussions and/or project presentations.

Attendance at classes is not mandatory.

In addition to the RGACC, we recommend reading other reference documents for the assessment process, such as the Regulations for Students with Special Status (REEE) and the Code of Academic Conduct (CCA).

By the end of this curricular unit, students should be able to:
LO1: Apply co-creation methodologies to develop innovative and sustainable projects.
LO2: Create empathy with the user and the organization by defining needs, obstacles, objectives, opportunities, and key project issues.
LO3: Conduct a literature review and critical analysis of the competitive landscape to underpin the problem definition.
LO4: Identify and integrate the digital, computational, and other resources necessary for project development.
LO5: Apply project planning and agile management knowledge to structure and implement the solution.
LO6: Engage in collaborative dynamics by delivering written and oral presentations that communicate progress and results.
LO7: Incorporate feedback from the first semester to refine the final solution, addressing previously identified limitations.
LO8: Consolidate and document the final project, ensuring its viability, robustness, and relevance.

SY1: Review and deepening of project planning and management methodologies in AI, emphasizing co-creation and agile management.
SY2: Presentation of case studies and project themes in digital AI technologies, focusing on the continuation of projects from PAIA-I.
SY3: Selection and refinement of the project theme, considering the organizational context and previously identified challenges.
SY4: Analysis of the problem space: creating empathy with the user and organization, defining and deepening the problem in view of business requirements and technological challenges.
SY5: Continuation of the literature review and critical analysis of the competitive landscape to underpin the proposed solution.
SY6: Identification and integration of the digital, computational, and other resources necessary for project development.
SY7: Development of the applied project, including implementation and improvement of the solution based on feedback, and communication of results.

This curricular unit adopts the model of assessment throughout the semester (ALS), without a final exam, centered on the execution of the project. Two deliverables will be assessed:

- R1: Intermediate Deliverable - Evaluates the progress of the work, corresponding to 40% of the final grade.
- R2: Final Deliverable - Presents the consolidated version of the project, integrating the work of the two semesters, corresponding to 60% of the final grade.

Each deliverable (R1 and R2) requires a minimum mark of 8.5. In each deliverable, an individual oral discussion may be required to assess mastery of knowledge at any time during the semester.

The deliverables consist of cumulative documents developed individually or in groups, and individual oral discussions, in public, and together with elements representing the companies / institutions, allowing the development and implementation of the proposed project to be assessed.

To pass the course, the student must achieve a final mark of at least 10 points, resulting from the weighted sum of all the assessment elements.

A student is considered to have failed if they do not pass the ALS.

The 1st Season and 2nd Season may be used to carry out assessment moments, such as oral discussions and/or project presentations.

Attendance at classes is not compulsory.

In addition to the RGACC, we recommend reading other reference documents for the assessment process, such as the Regulations for Students with Special Status (REEE) and the Code of Academic Conduct (CCA).