Machine Learning

Code:

CC4051

Acronym:

CC4051

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2020/2021 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master 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 language

English

Objectives

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

Presencial

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

Program

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.
Topics:
- 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.

Observations

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.