Machine Learning

Recommended Prerequisites

BSc in Informatics Engineering or equivalent.

Teaching Methods

Theoretical classes , 2h weekly, with audiovisual and computational means. Computational demonstrations studied techniques for machine learning.

Practical classes, for the development of mini-projects covering the several themes of the syllabus. Each mini-project occupies in average 2,5 classes and is developed in groups of 2 or 3 students. Some of the mini-projects have a research component, the students are challenged to search on the recent literature ideas for their implementation. The Matlab+Simulink + Toolboxes, and/or Weka are used for computer implementations.

Learning Outcomes

To study the main techniques of machine learning in the context of the multiplicity of data types available in practical applications. Namely, studies include the techniques such as decision trees, artificial neural networks and deep learning, fuzzy logic, fuzzy and neuro-fuzzy systems. To develop competencies to design systems for classification of large data sets, for diagnosis in industrial and medical contexts, for intelligent control, for holistic analysis of complex problems and critical evaluation of its results. Additionally, competencies for group working, for scientific and technical oral and written communication are developed. Generic competencies in analysis and synthesis, informatics knowledge relative to the study focus, problem solving, critical thinking, decision capability, autonomous learning, practical application of theoretical knowledge, creativity, self-criticism and self-evaluation, and research.

Work Placement(s)

No

Syllabus

Chap.1. Introduction to machine learning: the general process and its stages.

Chap.2. Decision trees: from ID3 algorithm to C.5 algorithm.

Chap.3. Clustering techniques.

Chap.4. Neurones and Artificial Neural Networks: single layer, multilayer and RBF and their learning algorithms.

Chap.5. Advanced NN architectures: recurrent networks and deep networks.

Chap.6. Fuzzy logic, fuzzy sets, fuzzy relations and Zadeh extension principle.

Chap.7. Fuzzy rule based systems of Mamdani and Sugeno types. Learning of fuzzy rules and training fuzzy systems.

Chap.8. Neuro-fuzzy systems: the ANFIS architecture and its training. Applications.

Head Lecturer(s)

António Dourado Pereira Correia

Assessment Methods

Assessment
Resolution Problems: 20.0%
Project: 30.0%
Exam: 50.0%

Bibliography

Foundations of Machine Learning , Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar MIT Press, 2012

Machine Learning, Tom Mitchell, McGraw-Hill, 1999

Machine Learning, An Algorithm Perspective, Marsland, Stephen, CRC Press 2008

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

Neural Network Design, Hagan, Demuth and  Beale, 2nd ed, 2014, ebook http://hagan.ecen.ceat.okstate.edu/nnd.html 

Deep Learning Toolbox Users´s Guide, The Mathworks, 2018

Fundamentals of Artificial Neural Networks,  Hassoun. M. H.,MIT Press, 1994.

Neural and Adaptive Systems,  J.C. Príncipe, N.R. Euliano, W. C. Lefevre, Wiley, 2000

Fuzzy Logic With Engineering Applications, 2nd Ed., Timothy Ross, McGraw Hill, 2004.

Fuzzy Logic Toolbox Users´s Guide, The Mathworks, 2018.

Introduction to Neuro-Fuzzy Systems, Robert Fullér, Springer Verlag  2000.

Neural Networks: A Comprehensive Foundation,Simon Haykin,Prentice Hall,1999

Fuzzy Modelling and Control, Andrzej Piegat, Springer Verlag, 2001.