CONTACT
CONTACT

Applications of Data Science and Artificial Intelligence

Course ID
CSIS-E5
Direction
2nd
Semester
Spring
Type
2rd direction elective

Learning Outcomes

Comprehensive Understanding of the foundational concepts, principles, and theories underlying data science and artificial intelligence. Technical proficiency in implementing and applying various machine learning algorithms, data preprocessing techniques, and advanced analytics tools. Problem-Solving Skills in analyzing complex problems and design effective solutions using datadriven and AI approaches.

Course Content

Foundations of Data Science and AI
Machine Learning Algorithms
Advanced Machine Learning
Big Data Technologies
Natural Language Processing (NLP)
Computer Vision
Ethical and Legal Aspects
Data Engineering
AI Applications in Industry
Research Methods in Data Science and AI
Emerging Trends in Data Science and AI

General Skills

Search, analysis and synthesis of data and information with the use of the assorted technologies

Adaptation in new conditions

Decision Making

Independent work

Social, work-related and ethical responsibility in matters related to gender equality.
φύλου

Learning and Teaching Methods - Evaluation

Teaching methods: On site
Use of ICT: email, e-class, Code development environments, Advanced software libraries.

Activity Work load
Semester
Lectures 26
Lab exercises 0
Thesis 56
Independent Study 68
Total 150

Assessment

Capstone project

Literature

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition)
John Paul Mueller and Luca Massaron, Machine Learning for Dummies
M. Tim Jones, AI Application Programming (Programming Series) 2nd Edition
Engineering Applications of Artificial Intelligence
The Journal of Machine Learning Research
Artificial Intelligence Review An International Science and Engineering Journal, Springer
IEEE Transactions on Artificial Intelligence
Taylor Francis, Applied Artificial Intelligence