Machine Learning

Code:

M.EEC006

Acronym:

APC

Keywords
Classification	Keyword
OFICIAL	Other Technical Areas

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EEC	44	Syllabus	1	-	6	39	162

Teaching language

English

Objectives

The aim of the course is to present some of the topics which are at the core of modern Machine Learning, from fundamentals to state-of-the-art methods. Emphasis will be put both on the essential theory and on practical examples and lab projects. Each exercise has been carefully chosen to reinforce concepts explained in the lectures or to develop and generalize them in significant ways. Upon the successful conclusion of the course, students should have the:

- Ability to work in team, organization and planning

-Ability to analyze and synthesize knowledge.

-knowledge of the fundamentals of machine learning.

-Ability to develop simple machine learning systems depending on existing needs and apply the most appropriate technological tools.

-Acquire the learning skills that allow continuing studying in a way that will be largely self-directed or autonomous.

Learning outcomes and competences

 Aquisition of a body of basic knowledge and the transmission of the very process of knowledge construction. The aim is to provide a solid preparation in mathematics skills in machine learning and prepare competences in modeling techniques fundamental for the processes of acquisition, processing and use of information.

 

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Previous knowledge:

Signal Processing, Programming

Program

1. Introduction to Learning Theory [Data driven process; Overfitting and generalization;
Taxonomy of the Learning Settings; Taxonomy of the Learning Tools; Representation, Evaluation,
Optimization]
Refresher on algebra, probabilities and statistics
2. Introduction to Linear Regression [Criterion (Evaluation); Normal Equation; The Least-Mean-
Square (LMS) method; Steepest descent; Ridge and Lasso regression]
Time-varying statistics: Kalman filtering.
Online learning.
3. Generative Classifiers [Optimal Bayes decision; Gaussian based classifier (linear and
quadratic);
Conditional Independence and Naïve Bayes classifier; Non-parametric density estimation:
Parzen window method]
4. Non-Generative Classifiers [Logistic regression]
Applications in ECE
5. Model Selection and evaluation
6. Introduction to Neural Networks
7. Introduction to Deep Neural Networks. Introduction to Convolutional Neural Networks.
8. Unsupervised Learning – Clustering [Clustering algorithms; Kmeans, kmedoids, soft kmeans;
Mixture of Gaussians; Manifold Learning (PCA, MDA, ISOMAP and LLE)]

Mandatory literature

Sergios Theodoridis; Machine Learning: A Bayesian and Optimization Perspective, Academic Press/Elsevier, 2020 (second edition)

Complementary Bibliography

Bishop Christopher M.; Pattern recognition and machine learning. ISBN: 978-0-387-31073-2
Duda Richard O.; Pattern classification. ISBN: 0-471-05669-3

Teaching methods and learning activities

Participatory lectures, seminars and conferences, learning based on the resolution of practical cases and projects, autonomous work and independent study by students, group work and cooperative learning.

Subjects will be covered both in participatory lectures, where students will have the chance to implement methods for themselves. During the lecture part, the course topics will be presented and discussed. The practical/lab periods will be used for solving exercises and for the development of the assignments.

Students will be assigned weekly individual homework assignments during the whole duration of the course, involving exercises, readings and summarization of selected texts. They will account for 30% of the final grade. Practical work will consist of one project covering the course topics. This will account for 40% of the final grade. The final exam will account for 40% of the final grade.

Software

Matlab
Python

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	40,00
Trabalho laboratorial	30,00
Participação presencial	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	40,00
Estudo autónomo	70,00
Frequência das aulas	52,00
Total:	162,00

Eligibility for exams

Students will be assigned weekly individual homework assignments during the whole duration of the course, involving exercises, readings and summarization of selected texts. They will account for 30% of the final grade. Practical work will consist of one project covering the course topics. This will account for 30% of the final grade.

Calculation formula of final grade

Students will be assigned weekly individual homework assignments during the whole duration of the course, involving exercises, readings and summarization of selected texts. They will account for 30% of the final grade.
Practical work will consist of one project covering the course topics. This will account for 30% of the final grade.
The final exam accounts for the remaining 40% of the final grade.

The final grade is valid only if the student achieves at least 40% of the maximum grade in the Exam component.

Special assessment (TE, DA, ...)

The students are submitted to the same type of evaluation and with the same rules as the other students with a normal statue

Classification improvement

In the same academic year, the students may apply to improve the mark for the written exam only.