Machine Learning Complements

Code:

M.EIC011

Acronym:

CAC

Keywords
Classification	Keyword
OFICIAL	Artificial Intelligence

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EIC	8	Syllabus	1	-	6	39	162

Teaching language

English

Objectives

The general aim of the course is to create skills in the treatment of complex data. The goal is to develop the ability to process data that are not simply table of i.i.d. observations. The types of complex data (CD) covered include those that are important today (graphs, and spatio-temporal data). However, the course will be flexible to accommodate new types or sources of data. Students will be prepared for the development of techniques for new types of data that they are confronted with in their professional lives.

Learning outcomes and competences

The learning outcomes are:

• understand the nature of common complex data types and their impact on data analysis methodologies, in particular regarding algorithms and evaluation.
• understand the most popular approaches as well as the state of the art for analyzing the most common types of complex data.
• configure and use technologies for analysis of complex data types.
• develop (create / adapt) methodologies for analysis of new types and sources of complex data.

Working method

Presencial

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

Program

• Advanced topics in recommender Systems


• Natural Language Processing/Text mining


• Social network analysis


• Time series analysis


• Analysis of spatio-temporal data


• Trends in analysis of complex data

Mandatory literature

David Easley; Networks, crowds, and markets
Robin Burke; Recommender systems
Daniel Jurafsky; Speech and language processing. ISBN: 0-13-095069-6 (https://catalogo.up.pt/F/?func=direct&doc_number=000954421)
Rob J Hyndman, George Athanasopoulos; Forecasting: principles and practice, 3rd edition, 2021 (https://otexts.com/fpp3/)

Teaching methods and learning activities

• Content presentation by the teacher.


• Solving theoretical and practical exercises by the students.


• Discussion of scientific papers.


• Seminars by other researchers.


• Written evaluation.

Software

R
python

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	45,00
Estudo autónomo	78,00
Frequência das aulas	39,00
Total:	162,00

Eligibility for exams

In case of a missing component of the evaluation, the respective grade is 0 (zero) values.

Minimum grade in the Project: 8.0 (out of 20)

Calculation formula of final grade

50% Exam + 50% Project

Minimum grade in the Exam: 8.0 (out of 20)

Special assessment (TE, DA, ...)

Students with worker or equivalent status, who are exempt from class attendance should, at regular intervals to be defined with the teachers, present the progress of their work, as well as do the scheduled presentations together with the regular students.

Classification improvement

Grade improvement may be done for the mini-test in the special season (recurso) of the year in which the student is approved.

For components which no grade improvement has been done in the year in which the student is approved, improvement may be made in one or more of the components in the following year, during the regular or special season.