Machine Learning

Recommended Prerequisites

Probabilities and Statistics, Linear Algebra, Calculus, Programming.

Teaching Methods

The curricular unit consists of theoretical-practical classes (TP) that provide a detailed presentation of concepts, principles, and fundamental theories using audiovisual media. Basic practical exercises are solved during these classes to consolidate the theoretical concepts. Additionally, practical laboratory classes (PL) are conducted, where students work on exercises that require the integration of various theoretical concepts and aim to foster critical reasoning skills.

The evaluation process encompasses all the topics covered in the course and focuses on assessing both the understanding of the fundamental theoretical concepts as well as the ability to solve complex problems from the real world.

Learning Outcomes

The course focuses on teaching algorithms that enable the exploration of machine learning applications, both supervised and unsupervised, for classification. It aims to formalize these algorithms and provide models that are applicable across various domains. The curricular unit works in parallel with the Deep and Reinforcement Learning curricular unit, operating in a complementary manner.

Work Placement(s)

No

Syllabus

1. Introduction

-Pipeline review

-Supervised vs unsupervised learning

-Classification, regression and generation

- Topics on features engineering

-Generalized decision functions

-Approaches for multi-class classification

2.Model evaluation

-Performance assessment measures

-Data partitioning

-The variance-bias dilemma.

-The problem of the curse of dimensionality

3.Unsupervised learning

-Partition clustering

-Hierarchical clustering

-Density based clustering

-Clustering based on combined gaussian models

4.Supervised learning

-Linear discriminants

-Probabilistic classification

-Non-parametric classification: kNN and SVM

-Rule-based models

5. Combination of classifiers

-Product and sum rule

-Boosting, Bagging and Stacking.

Head Lecturer(s)

César Alexandre Domingues Teixeira

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

1. Bishop, C.M., ?Pattern Recognition and Machine Learning?, Springer Verlag, 2006
2. Duda, R. O., Hart, P.E., and Stork, D.G., ?Pattern Classification, ? 2nd ed. Wiley Interscience (2001)
3. J.P. Marques de Sá, ?Pattern Recognition: Concepts, Methods and Applications?, 2001, XIX, 318 p., 197 illus., Springer-Verlag (2001)
4. M. N. Murty and V. S. Devi, ?Pattern Recognition: An Algorithmic Approach, Springer, 1st Edition., XII, 263 p. (2011)
5. Peter Flach, Machine Learning: the art and science of algorithms that make sense of data, Cambridge University Press, 2012.
6. Trevor Hastie, Robert Tibsshirani and Jerome Friedman, The Elements of Statistical Learning (2nd Edition), Springer, 2008.
7. Introduction to Machine Learning with Python, Andreas C. Muller and Sarah Guido, O'Reilly, 2017.
8. João Gama et alli, Extracção de Conhecimento de Dados, Edições Sílabo, 2012.