Laboratory IA and CD

Code:

CC3044

Acronym:

CC3044

Level:

300

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Artificial Intelligence and Data Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:IACD	62	study plan from 2021/22	3	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

Objectives: To provide students with skills for the development of AI and DS projects. This objective will be achieved through the development, in groups, of a project to address a real world problem, in contact with domain experts. This project will also serve to consolidate the knowledge and skills acquired as part of the other courses in the programme.

 

Learning outcomes and competences

Skills:

1. Structuring software development
2. Participate in a development team
3. Structuring the development and management of an AI/CD project
4. Communicate project results within the team and to third parties

Working method

Presencial

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

Advanced knowledge of programming and techniques and concepts of Artificial Intelligence and Data Science

Program

Theoretical classes will consist of seminars on topics related to Artificial Intelligence and Data Science projects, such as:

- Impact of AI and legal issues;

- Ethics in AI;

- Project management;

- AI engineering;

- MLOps;

- Application of AI in specific use cases;

- Philosophy/Psychology/Neurosciences and the relationship with AI;

- AI of the Future;



The practical classes will mainly be allocated to the execution of two projects related to AI and CD.

Mandatory literature

Virginia Dignum; Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (Artificial Intelligence: Foundations, Theory, and Algorithms), Springer, 2019. ISBN: 978-3030303709

Complementary Bibliography

Provost, F., & Fawcett, T. ; Data Science for Business: What you need to know about data mining and data-analytic thinking, O'Reilly Media, 2013. ISBN: 9781449361327
Joshua Eckroth; AI Blueprints: How to build and deploy AI business projects, Packt Publishing, 2018. ISBN: 9781788997973

Teaching methods and learning activities

- Project development topics: Met. software project development, IS/CD project development: methodologies, project management, team management, presentation of AI/CD results; model deployment.
- Group project development.
- Presentation of works

keywords

Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	100,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de relatório/dissertação/tese	40,00
Trabalho de investigação	22,00
Apresentação/discussão de um trabalho científico	2,00
Frequência das aulas	48,00
Trabalho laboratorial	50,00
Total:	162,00

Eligibility for exams

Submission of the assignments

Calculation formula of final grade

NF = 0.5*T1 + 0.5*T2



NF – Final Grade

T1 – Data science project

T2 – Artificial Intelligence Project

Special assessment (TE, DA, ...)

During special times, students who completed their work unsuccessfully in the previous academic year can resubmit their work, with appropriate improvements, and may be invited for a face-to-face presentation.

Classification improvement

There is no possibility for grade improvement