| Code |
14469
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
None.
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Learning outcomes |
The course aims to introduce students to the fundamentals, methods and applications of Machine Learning (ML).
The techniques, design methodologies and algorithms that illustrate supervised, unsupervised and reinforced approaches to ML are discussed.
Different algorithms and computational techniques used in modern ML are presented and the respective experimental application to real data is exemplified.
At the end of this course students should be able to:
- Understand the paradigms and challenges of the area of ML.
- Understand the motivations, assumptions and limitations of the various computational techniques that are applied to solve a particular problem in ML.
- Explore the existing implementations of the most popular ML algorithms and know how to implement and adapt them properly.
- Identify directions for research in ML.
- Show autonomy in the adoption and adaptation of ML techniques.
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Syllabus |
1. Introduction. Preliminary Concepts and Definitions: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Areas of application. Examples of classification, regression, and grouping problems.
2. Data processing topics. Distances and similarities. Measures of dispersion and visualization of data. Dimensionality reduction. Detection of anomalies.
3. Supervised Learning. Regression. Classification. Instance-based classifiers. Graphic models. Neural Networks. Decision Trees. Support Vector Machines. Ensembles.
4. Unsupervised Learning. Clustering Analysis. Partitioning methods. Probabilistic clustering methods. Fuzzy clustering methods. Hierarchical clustering methods. Self-organized maps.
5. Selected applications. Control systems, recommendation systems and large-scale machine learning.
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Main Bibliography |
David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2016.
Ian Goodfellow, and Yoshua Bengio, Deep Learning, MIT Press, 2016.
Andrew Ng, Machine Learning Yearning, 2018.
E. Alpaydin (2010). Introduction to Machine Learning, Second Edition, MIT Press.
Scientific papers and extra materials provided by the teacher.
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Teaching Methodologies and Assessment Criteria |
- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and practical classes
- Practical Projects (P) The practical projects of this course weight 50% (10/20) of the final mark.
- (P1) Practical Project 1: Supervised Learning (Linear Regression) (5/20);
- Due Date: Monday, October 10th, 23:59:59, 2023.
- (P2) Practical Project 2: Supervised Learning (Classification) (5/20);
- Due Date: Monday, October 31st, 23:59:59, 2023.
- (P3) Practical Project 3: Unsupervised Learning (5/20);
- Due Date: Monday, November 28th, 23:59:59, 2023.
- (P4) Practical Project 4: Reinforcement Learning (5/20);
- Due Date: Monday, December 19th, 23:59:59, 2023.
- To get approved at the course, a minimal mark of 8/20 should be obtained in the practical project part;
- Written Test (F) Tuesday, January 9th, 2023, 14:00, Room 6.18.
- Mark (M) M = [A >= 0.8] * (P * 10/20 + F * 10/20)
- Admission to Exams Students with M >= 6 are admitted to final
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Language |
Portuguese. Tutorial support is available in English.
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