C1 - Image acquisition and representation
C2 - Image operations
C3 - Extraction of image features
C4 - Introduction to machine learning
C5 - Artificial neural networks
C6 - Convolutional neural networks
C7 - Knowledge transfer
C8 - Network architectures for detecting and identifying image objects
C9 - Network architectures for automatic content generation

Given the imminently practical nature of the course there is no exam assessment modality - there are only assessment modalities carried out along the semester.

Modality A (requires attendance to at least 60% of the classes)
- Exercises (30%) - group work, includes participation on class activities (10%) and two work assignments (challenges) delivered online (10% each);
- Test (30%) - individual;
- Project (40%) - group work, but evaluated individually; includes report and oral discussion.

Modality B
- Test (45%) - individual;
- Project (55%) - individual or in a group, but evaluated individually; includes report and oral discussion.

The "Project" component requires a minimum of 10 (out of 20) values, regardless of the assessment modality.

The "Test" component is carried out at the end of the classes period; if a student misses the test with justified reason, he/she can take a new test during the exam’s season.

The grade of the "Project" component is subject to an oral discussion. This oral discussion may impose a limit on the project's grade if the performance shown by the student is below the quality of the delivered project; the student may fail if performance shown in the discussion is not sufficient.

There is no process for grade improvement.

The evaluation process in the special season is identical to modality B, but the "Project" must be carried out individually.

Mandatory

J. Howse, J. Minichino, Learning OpenCV 4 with Python 3, 3rd Edition, Packt Publishing, 2020, -, -
M. Elgendy, Deep Learning for Vision Systems, Manning, 2020, -, -

Optional

Tomás Brandão, Materiais da UC disponibilizados na plataforma de e-learning, 2024, -, -
M. Nixon, A. Aguado, Feature Extraction and Image Processing for Computer Vision, 4th Edition, Academic Press, 2019, -, -
I. Goodsfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016, -, -
Vários, Tutoriais e documentação da bibliotecas OpenCV, -, -, https://opencv.org/
Vários, Tutoriais e documentação da biblioteca Tensorflow, -, -, https://www.tensorflow.org/
R. Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition, Springer, 2021, -, https://szeliski.org/Book/
F. Chollet, Deep Learning with Python, 2nd Edition, Manning, 2021, -, -

The specific programme contents (PC) are as follows:
PC1. Description of the specific problem, issues to be investigated and the relevant context.
PC2. Designing a responsible development and research project plan.
PC3. Ethical, sustained and targeted implementation of the different project phases.
PC4. Critical analysis of results.
PC5. Communication of development and respective results in a replicable and ethically responsible manner.

Assessment focuses exclusively on the evaluation of the following elements:
1. Evaluation of a written dissertation presenting the research, the work carried out and an analysis of the respective results.
2. Evaluation of an oral presentation of the work.
3. Evaluation of the student's performance during the oral discussion of elements 1) and 2).
The assessment is carried out by a jury in public examination, after confirmation by the supervisor that the dissertation is complete and ready to be presented in public examination.
The assessment will be based on the scientific merit of the study and its theoretical and methodological adequacy.

Mandatory

Dependente dos temas específicos abordados pela/o (equipa) docente responsável pela orientação.

Optional

·

Syllabus contents (SC):
SC1- Formulate the starting question
SC2-Identify the relevant literature and elaborate a theoretical and empirical revision
SC3-Formulate the research problem and the hypotheses
SC4- Design a study to test the hypotheses
SC5- Carry out the study
SC6-Analyse and interpret the results
SC7-Elaborate the Master Project plan
SC8-Write the Master Project

A panel of judges in public tests will assess the Master Project, after the supervisor's approval of its
conclusion and quality to be presented in public tests. Assessment will be based on the scientific merit of the study and on its theoretical and methodological adequacy.

Mandatory

G. Garson (2001), Guide to Writing Empirical Papers, Theses, and Dissertations, Marcel Dekker Inc N. Bui, Yvonne (2014). How to write a Master's Thesis, Sage Publications, Inc.
Punch, F. Keith (2016), Developing effective research proposals, Sage Publications.

Optional

Punch, F. Keith (2016), Developing effective research proposals, Sage Publications.

CP1. Integrity and responsibilities in research.
CP2. Definition of the research topic and field.
CP3. Definition of the study’s subject, research problem, and objectives.
CP4. Conducting a literature review using appropriate methodologies.
CP5. Performing critical analyses of results: content and impact.
CP6. Scientific communication and dissemination methodologies.

This course unit focuses on establishing the initial chapters of the master's dissertation, thus it does not involve a 100% written exam.
Assessment:
(a) an individual presentation of their work (70%),
(b) a deliverable document that includes the definition of the research problem and objectives, context, research questions and objectives, a systematic literature review and discussion of the findings, and the design and planning of the dissertation project (30%).

Mandatory

> Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181:526-534.
> Saltz, J. S. (2021). "CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps," 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, pp. 2337-2344
> Blischak, J. D., Davenport, E. R., & Wilson, G. (2016). A Quick Introduction to Version Control with Git and GitHub. PLOS Computational Biology, 12(1).
> Thomas, C. G. (2021). Research methodology and scientific writing. 2nd edition. Springer.

Optional

> Perkel, J. M. (2018). A toolkit for data transparency takes shape. Nature, 560(7719), 513–515.
> ALLEA - The European Code of Conduct for Research Integrity, European Union, URL: https://allea.org/code-of-conduct/
> RGPD - Regulamento Geral de Proteção de Dados, URL: https://gdpr-info.eu OU https://gdpr.eu
> 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.

P1. Data-driven decision making.
P2. Types of Analytics.
P3. Data processing, modeling and visualization.
P4. Effective Business Presentation / communication; ability to explain complex analytical models and results.
P5. Power BI Analytics Platform.

Assessment throughout the semester:
a) Individual work I - ETL and Analytical Modeling (25%) - OA 1, 2
b) Individual work II – DAX and Data Visualization (45%) - OA 1, 2, 3
c) Online discussion of individual work I and II (30%) - LO 1, 2, 3
The evaluation requires: a) students attend at least 2/3 of classes, and b) a minimum of 10 points in the final classification.
Scale: 0-20 points.
The course has no final exam given its design nature applied to real situations.

Mandatory

Aspin, A., Pro Power BI Desktop: Self-Service Analytics and Data Visualization for the Power User, 2020, 3rd ed. Edition, Apress.,
Deckler, G. & Powell, B., Microsoft Power BI Cookbook: Convert raw data into business insights with updated techniques, use cases, and best practices, 2024, 3 dn Ed., Packt Publishing.
Microsoft, Microsoft Learn Power BI, n.a., Microsoft, https://learn.microsoft.com/en-us/training/powerplatform/power-bi
Albright, S. & Winston, W., Business Analytics: Data Analysis & Decision Making, 2019, 7th Edition, South-Western College Pub.
Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. & Silipo, R., Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data, 2020, 2nd Edition, Springer International Publishing.
Knaflic, C. N., Storytelling com dados: um Guia Sobre Visualização de Dados Para Profissionais de Negócios, 2019, Alta Books.

Optional

Janicijevic A., Power Query Cookbook, 2021, Packt Publishing.
Groot, R. & Korte, M., The Definitive Guide to Power Query (M), 2024, Packt Publishing.
Adamson C., Star Schema, 2010, McGraw-Hill Education.
Russo M. & Ferrari A., The Definitive Guide to DAX, 2dn Ed., 2019, Microsoft Press.
Russo M. & Ferrari A., DAX Patterns, 2dn Ed., SQLBI Corp.
Knaflic C., Storytelling with Data, 2015, Wiley.
McCandless, D., Knowledge is Beautiful, 2014, William Collins.
Bahga, A. & Madisetti, V., Big Data Science & Analytics: A Hands-On Approach, 2016, VPT.

PC1. Bayes Theorem and Bayesian paradigm
PC2. Graphical and hierarchical models
PC3. Bayesian inference
PC4. Bayesian optimization
PC5. Bayesian regression and classification
PC6. Bayesian latent factor models

Students may choose either Evaluation during the semester or Final exam.
Assessment throughout the semester:
- group work with minimum grade 8 (50%)
- individual test with minimum grade 8 (50%)
Approval requires a minimum grade of 10.
EXAM:
The Final Exam is a written exam. Students have to achieve a minimum grade of 10 to pass.

Mandatory

Reich, B. J., S. K. Ghosh (2019), Bayesian Statistical Methods, Boca Raton: Chapman and Hall/CRC
McElreath, R. (2020), Statistical Rethinking: A Bayesian Course with Examples in R and Stan, CRC Press.
Levy, R., Mislevy, R. J. (2016), Bayesian Psychometric Modeling, 1st Edition. Boca Raton: Chapman and Hall/CRC
Kruschke, J. K. (2015), Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.

Optional

Durr, O., B. Sick (2020), Probabilistic deep Learning, Manning Publications Co.
Theodoridis, S. (2020),Machine Learning: A Bayesian and Optimization Perspective, Elsevier Ltd.
Martin, O., R. Kumar, J. Lao (2022), Bayesian Modeling and Computation in Python, CRC Press.
Heard, N. (2021), An Introduction to Bayesian Inference, Methods and Computation, Berlin: Springer Cham.
Albert, J., H. Jingchen (2020), Probability and Bayesian Modeling, Boca Raton: CRC Press/Taylor & Francis Group.
Códigos R / python
Slides aulas

The specific program contents (PC) may change or be adjusted depending on the availability of guest seminars. However, there are highlevel contents that will have to be addressed in the light of current knowledge, such as:
CP1. History of Data Science.
CP2. Visual and Narrative Information and Perception.
CP3. Cyberlaw in Data Science.
CP4. Ethically Responsible Artificial Intelligence.
CP5. Data Science in Economics and Management.
CP6. Data Science in Health.
S7. Data Science in the Humanities.
CP8. Data Science in Society.
CP9. Data Science for the Future.

As this is a seminar course, there should be no written exam.
Assessment will take place throughout the semester, with students participating in small collaborative working groups (E1), preparing individual seminar sheets (E2), and carrying out research into a problem related to one of the themes covered in the different seminars with (i) a final oral presentation and (ii) a digital infographic element (E3).
Elements E1 and E2 will be assessed by the coordinator, while E3 will be assessed by peers, moderated by the coordinator.
The final mark will be calculated as: 0.3 E1 + 0.3 E2 + 0.4 E3.

Mandatory

Dependente dos temas específicos abordados pelo responsável do seminário semanal.

Optional

Voeneky, S., Kellmeyer, P., Mueller, O., & Burgard, W. (Eds.). 2022. The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives. Cambridge: Cambridge University Press. Dignum, V. 2019. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer Publishing Company, Incorporated, 1st edition,. ISBN 3030303705. Conitzer, V., Sinnott-Armstrong, W., Schaich Borg, J., Deng, Y., & Kramer,M. Moral decision making frameworks for artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), Feb. 2017. doi: 10.1609/aaai.v31i1.11140. URL https://ojs.aaai.org/index.php/AAAI/ article/view/11140. Domingos, P. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2015. ISBN 978-0465065707. Karachalios, K., Stern, N. & Havens,J. C. White paper - measuring what matters in the era of global warming and the age of algorithmic promises. Measuring What Matters in the Era of Global Warming and the Age of Algorithmic Promises, pages 1–17, 2020 van den Hoven, J. 2019. Design for values and values for design. Information Age, Journal of the Australian Computer Society, pages 4 – 7.

Introduction
CP1: Importance of large quantities of text, challenges and current methods
CP2: Unstructured vs. (semi-)structured information
CP3: Obtaining and filtering information, information extraction and Data Mining
Document Representation
CP4: Document pre-processing
CP5: Feature extraction: terms as features
CP6: Term weighting schemes
CP7: Vector space models
CP8: Similarity measures
Natural Language Processing
CP9: Language models
CP10: Morphology and part-of-speech tagging
CP11: Complex structures: syntactic analysis
CP12: Information extraction
Text Classification
CP13: Introduction to statistical machine learning
CP14: Evaluation
CP15: Generative classifiers
CP16: Discriminative classifiers
CP17: Unsupervised learning
CP18: Text Mining Resources
Case Study
CP19: Sentiment analysis
CP20: Topic classification and identification

This course uses only assessment throughout the semester and does not include exams.
Assessment components:
a) TESTS (2 mini-tests: 5% each, final test: 40%), taken during the course period;
b) PROJECT (50%).
The TESTS grade can be replaced by a written test to be taken in the assessment period corresponding to the 1st season, 2nd season or special season (Art. 14 of the RGACC).
The PROJECT grade is limited to the TEST grade + 6 points.
Students may improve their grade in the TESTS component by taking a written test during the assessment period corresponding to the 1st season. Students wishing to do so must inform the teachers as soon as the assessment throughout the semester marks are published.

Mandatory

* Machine Learning for Text (2018). Charu C. Aggarwal. https://doi.org/10.1007/978-3-319-73531- 3
* An Introduction to Text Mining: Research Design, Data Collection, and Analysis 1st Edition (October 11, 2017). Gabe Ignatow, Rada F. Mihalcea. SAGE Publications. https://methods.sagepub.com/book/an-introduction-to-text-mining
* Speech and Language Processing (3rd ed. draft, 2024), Dan Jurafsky and James H. Martin. Conteúdo disponível em: https://web.stanford.edu/~jurafsky/slp3/

Optional

* Natural Language Processing for Social Media, Second Edition. Synthesis Lectures on Human Language Technologies. Morgan & Claypool, 2017. Atefeh Farzindar and Diana Inkpen. https://link.springer.com/book/10.1007/978-3-031-02167-1
* Jacob Eisenstein. Introduction to Natural Language Processing. Adaptive Computation and Machine Learning. The MIT Press, 2019. https://mitpress.mit.edu/9780262042840/introduction-to-natural-language-processing/

P1. Artificial Neural Networks (ANN) (2 lectures)
P1.1. Perceptron. Activation function
P1.2. MLP (multi-layer perceptron) and Backpropagation
P1.3. Loss function, learning/training an ANN
P1.4. Regularization, hyperparameter tuning
P2. Time series and sequential data (2 lectures)
P2.1. Basic concepts
P2.2. Trends and seasonality
P2.3. Stationarity, unit root tests, Granger causality
P2.4. ARMA/ARIMA/SARIMAX models
P2.5. Residual assumptions, diagnostic tests
P2.6. Prophet model
P2.7. Forecasting, measuring the forecast accuracy
P3. Deep Learning (4 lectures)
P3.1. Neural networks for time series
P3.2. Recurrent neural networks: RNN, GRU, LSTM,
P3.3. Forecasting: direct, recursive, rolling windows
P4. Programming/computing with Python
P5. Application of the studied concepts: information and value extraction from real-world data (2 lectures)

The periodic evaluation includes the realization of:
a) An individual test (50%).
b) A teamwork (50%).

The periodic evaluation requires that students attend at least 80% of classes. The test covers all topics.

In this type of evaluation, students must achieve a minimum grade of 9.5 in the individual test and 10 in the teamwork. Otherwise, students must take a final exam (minimum approval score: 10).

Mandatory

Ficheiros (slides e scripts) da UC a disponibilizar no e-learning/Fenix
Yves Hilpisch (2018), Python for Finance, 2nd Edition, O.Reilly Media, Inc.
Tarek A. Atwan, (2022), Time Series Analysis with Python Cookbook, Packt Publishing.
Mills, T.C. (2019), Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, Academic Press, Elsevier Inc.
Brooks, C., (2019), Introductory econometrics for finance, 4nd ed., Cambridge University Press.

Optional

Edward Raff, (2022), Inside Deep Learning: Math, Algorithms, Models, Manning Publications Co.
Louis Owen, (2022), Hyperparameter Tuning with Python, Packt Publishing.
James Ma Weiming, (2019), Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition, Packt Publishing.
Juselius, K., (2006), The Cointegrated VAR Model: Methodology and Applications, Oxford University Press.