Artificial Intelligence and Data Science

Code:

CC1023

Acronym:

CC1023

Level:

100

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2022/2023 - 2S

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	93	study plan from 2021/22	1	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

This will address the state-of-the-art topics of Artificial Intelligence (AI) and Data Science (DS), giving students a technical knowledge, although not in-depth, about its concepts, problems and applications.

Regarding the AI ​​and DS areas, the objectives of the course are:

- Provide a historical perspective of its emergence and evolution.
- Identify its relevance and impact in the society.
- Study the relationship with other sciences and interactions with society.
- Know the different stages of development processes.
- Ability to develop small prototyping projects in AI and DS.

Learning outcomes and competences

Development of the following skills:

1- ability to identify general concepts, problems and applications of Artificial Intelligence (AI) and Data Science (DS) related to current and historical issues of artificial intelligence and data science, and their relationship with science, society and innovation, as well as their impacts.

2- ability to describe, debate and critique AI and DS interactions with society.

3- ability to develop small projects in AI and DS.

Working method

Presencial

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

Python programming skills.

Program

In this course, fundamental and current concepts about AI and DS will be presented and discussed.

Syllabus:
1. Definition of Artificial Intelligence and Data Science. The relevance of AI and DS in science, society and innovation.
2. Brief history and concepts of AI and DS and their relationship with Computer Science and Mathematics.
3. Introduction to AI. AI areas, types of problems and paradigms for solving problems through AI.
4. Introduction to Data Science.  Importance of the data; Different roles and steps of a DS project. Set expectations for a DS project.
5. Impact of AI on society (ethics, privacy, work, trust, interpretability, security).
6. Introduction to the main AI and DS technologies through examples of known applications.
7. Development of a small AI and/or DS project.

Mandatory literature

Stuart Russell and Peter Norvig; Artificial Intelligence: A Modern Approach, Pearson, 2021. ISBN: 9780134671864
Jean-Louis Laurière; Problem-solving and artificial intelligence. ISBN: 0-13-711748-5
Pedro Domingos; The Master Algorithm., Penguin Books, 2015. ISBN: 978-1501299384
Virginia Dignum; Responsible Artificial Intelligence, Springer, 2019. ISBN: 978-3-030-30370-9

Teaching methods and learning activities

Theoretical-practical classes with an expository component, a component of presentations by guest speakers, a discussion component and a practical component of prototyping a small project.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	50,00
Participação presencial	10,00
Teste	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	56,00
Frequência das aulas	56,00
Trabalho laboratorial	50,00
Total:	162,00

Eligibility for exams

To obtain attendance it is necessary:

1) obtain a grade above zero in both works: Laboratory Work and Written Work.
2) Do not miss more than 4 practical classes (unless an exception is granted).

Calculation formula of final grade

FinalGrade = max( 0.2xMT1 + 0.2xMT2 + 0.1Kah + 0.25xT1 + 0.25T2;   0.25xMT1 + 0.25xMT2 + 0.25xT1 + 0.25T2)

In case of Exam, it corresponds to the sum of the two mini-tests, i.e. 40%

• MT1 - Mini-Test 1


• MT2 - Mini-Test 2


• Kah - Kahoot


• T1 - Assignment 1


• T2 -Assignment 2

Only the grade of the exam/mini-tests can be improved. Kahoots and practical assignments are not improved.

 Kahoots are calculated as:

Examinations or Special Assignments

N/A

Internship work/project

N/A

Special assessment (TE, DA, ...)

Students with special circumstances should discuss and negotiate their situation with the responsible of the course.

Classification improvement

The exam grade can be improved in the appeal period. The practical part cannot be improved.

Observations

Jury:

Pedro Gabriel Ferreira

Ana Paula Tomás

Luis Paulo Reis