Introduction and fundamental concepts revision
P1: Revisiting Neural Networks
- Feed Forward networks and backpropagation
- Regularization Techniques: Dropout, weight decay, early stopping
- Hyperparameter Tuning: Grid search, random search, Bayesian optimization
P2: Sequential data
- Recurrent Neural Networks
- Conditional sequence models
- LSTMs
- CNNs for sequential data
- Attention Mechanisms in sequential models
P3: Transformers
- Transformer architecture and attention
- Processing Natural Language
- Transformer Language Models
- Multimodal Transformers
- Applications, dialog systems, recent trends
P4: Generative Modeling
- Generative Adversarial Networks (GANs)
- Variational autoencoders (VAE)
- Autoregressive models

Assessment throughout the semester: Mini-tests (15%) + Project (35%) + Final Test (50%), held during the 1st exam period (simultaneously with the exam). A minimum grade of 8 (out of 20) is required in each assessment component.

Students may take the final exam (1st, 2nd, or special exam periods) if they choose this assessment mode or if they do not achieve a passing grade in the continuous assessment. The final exam consists of an individual test covering all course content.

The final grade for the Project is assigned individually to each student, based on an oral examination, and depends on the code developed, the report presented, and the student’s performance in the oral. Test questions may address aspects related to the Project.

Mandatory

- Deep Learning: Ian Goodfellow, Yoshua Bengio, Aaron Courville 2016 MIT Press
- Deep Learning - Foundations and Concepts, Christopher M. Bishop , Hugh Bishop, Springer 2024, ISBN 978-3-031-45467-7
- Probabilistic Machine Learning: An Introduction, Murphy, Kevin, (2022), https://probml.github.io/pml-book/book1.html

Optional

- Eli Stevens, Luca Antiga, Thomas Viehmann (2020) Deep learning in Python/ Pytorch, Manning Publications (Free book)
- Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, O’Reilly, 2017

(P1) Structuring a dissertation;
(P2) Systematic literature review;
(P3) Presentation of results;

This course has a final assessment by jury, on submission of the thesis.
The assessments made during the semester are only formative.

Mandatory

Slides da disciplina

Optional

Mandatory

Optional

1. Definitions and assumptions regarding the cognition-emotion debate
1.1. Cognition: notions of cognitive representation and of information processing; overview of cognitive functions
1.2. Emotion and related concepts
1.3. Historical and philosophical aspects of the cognition-emotion debate
1.4. Methodological approaches to the study of cognition-emotion interactions
2. The influence of emotion on cognition
2.1. Judgment, decision-making and processing modes
2.2. Attention and cognitive control
2.3. Memory
2.4. Language
2.5. Emotional traits and cognitive performance
2.6. Affective disorders
3. The influence of cognition on emotion
3.1. Cognitive processes and emotion regulation
3.2. Impact of cognitive disorders on emotion
4. Integration of cognitive and emotional processes: interactions in the brain

Assessment throughout the semester: Group assignment, including the discussion of a paper and a written report on the topic (40%); test (60%). Students will get approval if they achieve at least 9.5 points in each of the evaluation elements.

Students who fail or miss evaluation throughout the semester are eligible for the exam (100%). They will be approved if their grade is >= 9.5.

Mandatory

Eysenck, M. W., & Keane, M. T. (2020)Cognitive psychology: A student’s handbook (8th Edition). Routledge: Taylor & Francis Group
Storbeck, J., & Clore, G. (2007). On the interdependence of cognition and emotion. Cognition and Emotion, 21, 1212-1237
Power, M. J., Dagleish, T. (Eds., 2016). Handbook of cognition and emotion: From order to disorder (Third Edition). London: Psychology Press
Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9, 148-158.
Okon-Singer, H., Hendler, T., Pessoa, L., & Shackman, A. (2015). The neurobiology of emotion?cognition interactions: fundamental questions and strategies for future research. Frontiers in Human Neuroscience, 9, 58.
Ochsner, K. N., & Phelps, E. (2007). Emerging perspectives on emotion-cognition interactions. Trends in Cognitive Sciences,11(8), 317-318.
Duncan, S., & Barrett, L. F. (2007). Affect is a form of cognition: A neurobiological analysis. Cognition and Emotion, 21, 1184-1211.

Optional

·

PC1-Introduction and review
1.1- Introduction
1.2 - Linear and quadratic programming
1.3 - Differential Calculus, Unrestricted and Restricted Optimization.
1.4 - Numerical methods

PC2 - Gradient descent and variations
2.1 - Gradient Descent
2.2 - Stochastic Gradient Descent
2.3 - Momentum
2.4 - Adagrad
2.5 - Others

PC3 - Metaheurísticas
3.1- Simulated Annealing
3.2 - Tabu Search
3.3 - Evolutionary Algorithms

The modes of assessment are:
1) Assessment throughout the semester:
- 1 mini-test (15%), completed individually during the semester;
- 1 research project (45%) on one or more topics of the course unit, carried out in groups of 2 or 3 students (including a report, code, and oral presentation during the semester);
- 1 individual written test (40%), to be taken during either the 1st or 2nd exam period.

Approval through continuous assessment requires a minimum grade of 8.0 out of 20 in all components.

2) Assessment by Examination: written exam (100%), to be taken during either the 1st or 2nd exam period.

The minimum passing grade for this course is 9.50 points (out of 20), rounded to a final grade of 10 points.

Mandatory

Sra, Suvrit, Sebastian Nowozin, and Stephen J. Wright, eds., Optimization for machine learning, Mit Press, 2012.

Dréo, Johann, et al., Metaheuristics for hard optimization: methods and case studies, Springer Science \& Business Media, 2006.

Optional

Dive into DeepLearning, Chapter 11, Optimization algorithms, https://d2l.ai/chapter_optimization/

Postek, Krzysztof and Zocca, Alessandro and Gromicho, Joaquim and Kantor, Jeffrey, Hands-On Mathematical Optimization with AMPL in Python, 2024. https://ampl.com/mo-book

CP1. Historical notes on Machine Learning. Relationship with other displines. Applications.
CP2. Machine Learning problems and approaches;
CP3. Unsupervised Learning;
CP4. Supervised Learning (symbolic and sub-symbolic);
CP5. Reinforcement Learning;
CP6. Search methods and Genetic/Evolutionary Algorithms;
CP7. Data pre-processing, results validation;
CP8. Speedup of ML algorithms;
CP9. ML algorithm implementation.

It is only possible to pass this course by assessment throughout the semester, this course has no exam.

Assessment elements:
- 4 practical exercises, code and report, in groups of 2 (10% each) during the academic term with face-to-face discussion;
- 1 test (20%), during the academic term;
- group project (40%) which includes a report, code and oral presentation

The assessment in the special season will consist of the project carried out individually and, as in the other terms, to be handed in one week before the special term and a written test which replaces the component of the practical exercises and mini-tests. The weights of these assessment elements are the same as those indicated above.

Attendance is not used as an evaluation or failure criterion.

Mandatory

Ethem Alpaydin, Introduction to Machine Learning, Fourth Edition, 2020, https://mitpress.mit.edu/9780262043793/introduction-to-machine-learning/

Optional

- Tom Mitchell, Machine Learning, 1997, http://www.cs.cmu.edu/~tom/mlbook.html
- Simon Haykin, Neural Networks and Learning Machines, Third Edition, 2009, https://cours.etsmtl.ca/sys843/REFS/Books/ebook_Haykin09.pdf
- R. Duda and P. Hart, Pattern Classification and Scene Analysis., 1973, https://www.amazon.com/Pattern-Classification-Scene-Analysis-Richard/dp/0471223611

The syllabus is as follows:
PC1: Object-based representation
PC2: Structured Descriptions
PC3: Ontologies and Knowledge Domain Representation
PC4: Knowledge Representation in a Social Context (Semantic Web)
PC5: Logic Programming
PC6: Non-monotonic Logic and ASP-Answer Set Programming
PC7: Uncertainty and Degrees of Belief
PC8: Abductive Reasoning
PC9: Qualitative Reasoning
PC10: Constraint Satisfaction
PC11: Representation and Reasoning by Actions and Plans
PC12: Abstraction, Reformulation and Approximation

In the assessment throughout the semester, students will have to take:
- Individual written test on the entire course syllabus (60%) - to be taken in the exam season (1st or 2nd exam).
- Research work (in groups) on one of the UC topics, with a report and oral presentation (40%). The oral presentation is made in class time during the semester. The grade for the research paper is divided 50% between each component and group members can have different grades.
Both assessment components in the semester-long assessment have a minimum mark of 8.

Alternatively, students can take only one exam (100%), which can be on both exam dates.
In the special exam period, students take the exam (100%).

Mandatory

--

Optional

I - Linear algebra, probabilities and information.
1. Inner products and matrix decomposition.
2. Principal components analysis.
3. Random variables, information and entropy.
4. Kernel methods and Gaussian processes in regression and classification.

II - Signal processing.
1. Discrete Fourier transform, FFT and convolution.
2. Discrete Wavelets.
3. Applications to sound and image processing.

III - Dynamic programming and reinforcement learning.
1. The context of reinforcement learning.
2. Bellman’s equation.
3. Iterative methods and applications.
4. Monte Carlo methods.

The assessment in this course will follow exclusively the modality of assessment throughout the semester, due to its strong practical component. A minimum attendance of 50% of classes is required.
Assessment will consist of 2 worksheets, 2 projects, and a final written test.
Each worksheet is worth 10% of the final grade (together worth 20%), and the worksheets are completed individually in writing.
Each project is worth 15% of the final grade (together worth 30%), and must be developed and implemented in Python. Projects are preferably completed in groups of three and will be subject to a final discussion.
The final written test (worth 50%) coincides with the first and/or second exam periods and covers all course material, excluding any Python implementation. To obtain approval at this course a minimum grade of 8.0 points (out of 20) is required in the final written test.
The special exam period is reserved for the cases provided for in Article 14 of the General Regulation for the Assessment of Knowledge and Skills (RGACC).

Mandatory

1. Ian Goodfellow, Yoshua Bengio e Aaron Courville, "Deep learning", MIT press, 2016.
2. Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
3. Richard S. Sutton and Andrew G. Barto, "Reinforcement Learning: an introduction", MIT press, 2nd edition, 2018.
4. Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola, "Dive into Deep Learning", 1st Edition, Cambridge University Press, 2024.

Optional

1. Francois Chollet, "Deep Learning with Python", Second Edition, Manning Publications Co., 2021.
2. Steven L. Brunton and J. Nathan Kutz, “Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control”, Cambridge University Press, 1st edition, 2019.

P1. CRISP-DM
P2. Introduction to scientific paper writing
P3. Introduction to project organization and management
P4. IA project practice

Assessment throughout the semester.

Presentation of a group project at the end of the term (50%) and monitoring of the project and interim presentations assessed individually (50%).

If the student is entitled to EEF, they can submit an individual project at that time with a discussion. This subject does not have a final exam.

Mandatory

F. Martínez-Plumed et al., "CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3048-3061, 1 Aug. 2021, doi: 10.1109/TKDE.2019.2962680. keywords: {Data mining;Data science;Data models;Trajectory;Business;Knowledge discovery;Standards;Data science trajectories;data mining;knowledge discovery process;data-driven methodologies}

Optional

Wortman-Wunder, Emily, & Kate Kiefer. (1998). Writing the Scientific Paper. Writing@CSU. Colorado State University. https://writing.colostate.edu/resources/writing/guides/, consulted 25-jun-2024.

1. Linear regression.
2. Linear classifiers.
3. Dense neural networks and backprop.
4. Universal approximation theorems and asymptotic behavior.
5. Automatic differentiation.
6. Introduction to TensorFlow and Keras.
7. Regularization techniques.
8. Convolution networks.
9. Data augmentation and fine-tuning.
10. Introduction to deep reinforcement learning.

Students must choose between two assessment models:

Model 1 (Regular Assessment):
Consists of two individual, in-person practical assessment moments (each worth 50%). In these assessments, students will be asked to adapt and modify the code developed during class, complemented by their own independent work. This independent preparation is organized through weekly worksheets.

Model 2 (Grade Improvement):
If students are not satisfied with their results from Model 1, they may complete a group project (with up to 4 members), which will account for 30% of the final grade. The remaining 70% will be based on the results from the Regular Assessment.

Note: All assessment moments may be subject to an oral discussion.

Mandatory

- François Chollet, “Deep Learning with Python”, Manning, Second Edition 2021.
- Simon J. D. Prince, "Understanding Deep Learning", MIT press, 2023.

Optional

- Ian Goodfellow and Yoshua Bengio, and Aaron Courville, "Deep Learning", MIT press, 2016
- Maxim Lapan, “Deep Reinforcement Learning Hands On”, Packt, Second Edition 2020.

S1 Introduction: a brief history of AI
S2 How AI works
S3 Bias, Ethics, Fairness, and Privacy, Robustness; Thrustworthiness;
S4 Regulatory frameworks
S5 AI for the present: case studies
S6 The interpretability challenge: XAI
S7 AI for the future: special topics in AI

This course is mainly concerned with cutting-edge concepts and methods in a state of the art that is still evolving, it does not require a 100 per cent written exam.
Students are assessed using the group work in the workshops (GW), by reading a conference/journal article and presenting it to the peers at the end of the semester (RP), and writing an individual research paper (IR) to be delivered ar at specific date during the 1st examination period. The grade is calculated by: 0,3 x GW + 0,3 x RP + 0,4 x IR.

Students are assessed through:
(a) group work in workshops (GW),
(b) presenting a conference or journal paper (RP) to peers (at the end of the semester)
(c) an individual research paper (IR) (on a set date in the 1st term),
according to the formula: 0.3 x GW + 0.3 x RP + 0.4 x RI.
For approval, any assessment element requires a minimum mark of 8.

Mandatory

S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. Pearson, Upper Saddle River, NJ, 2009.

A. Holzinger, P. Kieseberg, E. Weippl & A Min Tjoa. Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI. Springer Lecture Notes in Computer Science LNCS 11015. Cham: Springer, pp. 1-8, 2018.

C. Molnar. Interpretable machine learning. A Guide for Making Black Box Models Explainable, 2019. https://christophm.github.io/interpretable-ml-book/.

T. Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38, 2019.

Optional

ALLEA The European Code of Conduct for Research Integrity, European Union, URL: https://allea.org/code-of-conduct/
Artificial intelligence act, URL: https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf
Material de leitura a determinar ao longo das aulas e de acordo com os temas a tratar, nomeadamente: artigos em revistas científicas/artigos de opinião/livros para discussão em aula.