Machine Learning (11157)

Machine Learning (2021/2022) - Departamento de InformÃ¡tica

Description

Knowledge

Understand the paradigms and challenges of Machine Learning, distinguishing Supervised, Unsupervised and Reinforcement learning.
Learn the fundamental methods and their applications in data oriented knowledge discovery. Understand data features, the selection of models and their complexity.
Understand the advantages and disadvantages of the different methods.

Applications

Implement and adapt Machine Learning algorithms;
Model real data experimentally.
Interpret and evaluate experimental results.
Validate and compare different Machine Learning algorithms.

Soft Skills

Evaluate the suitability of each method to concrete applications and data sets.
Critical evaluation of the results.
Autonomy and self-reliance in the application and furthering studies in Machine Learning.

Syllabus

Introduction to Machine Learning

Machine Learning paradigms: Supervised Learning, Unsupervised Learning and Reinforcement Learning.

Data

2.1 Types of Data

2.2 Measures of similarity and dissimilarity

2.3 Data normalization and visualization

2.4 Dimensionality reduction by Principal Component Analysis

Supervised Learning

3.1 Regression

3.2 Decision Trees

3.3 Artificial Neural Networks

3.4 Support Vector Machines

3.5 Graphical models

3.6 K-nearest neighbour classifier

3.7 Methods for classifier evaluation and comparison

3.8 Ensembles

Unsupervised Learning

4.1 Partitional clustering

4.2 Probabilistic clustering

4.3 Partitional Fuzzy clustering

4.4 Hierarchical clustering

4.5 Markov chain

4.6 Clustering evaluation methods

4.6 Other unsupervised learning topics

Bibliography

T. Mitchell. Machine Learning, McGraw-Hill, 1997.

C. M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.

E. Alpaydin. Introduction to Machine Learning, Second Edition, MIT Press, 2010.

Stephen Marsland. Machine Learning: An Algorithmic Perspective CRC Press, 2011

Student work

	Hours per credit		28
	Hours per week	Weeks	Hours
Aulas prÃ¡ticas e laboratoriais			24.0
Aulas teÃ³ricas			24.0
AvaliaÃ§Ã£o			8.0
Self study			60.0
Project			52.0
Total hours			168
ECTS			6.0