Go to:
Esta página em português Ajuda Autenticar-se
You are in:: Start > CC4051

Site map
FC6 - Departamento de Ciência de Computadores FC5 - Edifício Central FC4 - Departamento de Biologia FC3 - Departamento de Física e Astronomia e Departamento GAOT FC2 - Departamento de Química e Bioquímica FC1 - Departamento de Matemática

Machine Learning

Code: CC4051     Acronym: CC4051     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 2S Ícone do Moodle Ícone  do Teams

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Data Science

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
E:BBC 18 PE_Bioinformatics and Computational Biology 1 - 6 42 162
M:CTN 1 Official Study Plan since 2020_M:CTN 1 - 6 42 162
M:DS 24 Official Study Plan since 2018_M:DS 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Alípio Mário Guedes Jorge

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 2 6,00
Inês de Castro Dutra 1,50
Alípio Mário Guedes Jorge 3,00
Mais informaçõesLast updated on 2021-04-05.

Fields changed: Components of Evaluation and Contact Hours, Obtenção de frequência

Teaching language



Students should be aware of the algorithmic fundamentals of machine  learning, as well as of the techniques for the resolution of the chalenges posed by each data set. They should be able to select the appropriate algorithms for each problem and apply the algorithms to new datasets and understand and evaluate their results.

Learning outcomes and competences

- Understanding the fundamentals of machine learning algorithms and methodologies presented
- Ability to justify the choice of a machine learning solution to a given problem
- Ability to apply the algorithms to new data sets
- Ability to evaluate results

Working method


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

Programming knowledge preferably in R or Python. Experienced programmers in other languages will not have any problems.

Knowledge of data processing with files and in SQL databases

Knowledge of statistical inference

Knowledge of basic matrix algebra and calculus in R and R ^ n


In this course we  will (re) visit fundamental concepts and algorithms for model learning and pattern discovery. There will be a focus on their justified application and example-driven experimentation.
- Introduction to the area: what is machine learning
- Simple classification and regression models (linear models and nearest neighbor models) and their validation: learning paradigms, loss functions, bias error and variance.
- Model Evaluation
- Inference methods of models: Search, Expectation-maximization, aggregation.
- Kernel Methods
- Neural networks, deep models and representation learning 
- Matrix Factorization
- Unsupervised, semi-supervised and poorly supervised pattern discovery.

Mandatory literature

Hastie Trevor; The elements of statistical learning. ISBN: 0-387-95284-5
Bishop Christopher; Pattern recognition and machine learning. ISBN: 0-387-31073-8

Teaching methods and learning activities

The classes will be partly expositive, with individual and group dynamics involving the students. Practical out-of-class work will be done with classroom support. Students will also be able to do writing and presentation work. There will be a final exam.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 40,00
Participação presencial 5,00
Trabalho prático ou de projeto 55,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 86,00
Frequência das aulas 42,00
Elaboração de projeto 32,00
Total: 160,00

Eligibility for exams

Attend at least 16 classes (except if an exception is granted)
Assignment grade above zero

Calculation formula of final grade

F = min( 0,4*E +0,55*P  + 0,05*A ; E*1,2 )

F: final grade
E: Exam
P: Practical components / assignments
A: Effective Attendance

Special assessment (TE, DA, ...)

Students under special circumstances should negotiate their situation with the responsible.

Classification improvement

The exam grade can be improved. The assignments grade cannot be improved after submission.


All the materials of the UC are in moodle.

The materials will be all in English, including the exams. Classes will be taught in English only if this is convenient. Students can participate / respond using Portuguese or English.
Recommend this page Top
Copyright 1996-2022 © Faculdade de Ciências da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z  I Guest Book
Page created on: 2022-08-18 at 07:23:29 | Reports Portal