University of West Attica

Artificial Intelligence and Data Science

Introduction to Artificial Intelligence

Course info:

Semester: 3

General Foundation

ECTS: 6

Hours per week: 3

Professor: T.B.D.

Teaching style: Face to face, use of specialized software

Grading: Homework/Projects (60%), Written exam (40%)

Activity Workload
Lectures 36
Class assignments / projects 42
Independent study 72
Course total 150

Learning Results

Upon successful completion of the course, students will be able to:

  • Gain a historical perspective of AI and its foundations.
  • Become familiar with basic principles of AI toward problem solving, inference, perception, knowledge representation, and learning.
  • Investigate applications of AI techniques in intelligent agents, expert systems, etc.
  • Explore the current scope, potential, limitations, and implications of intelligent systems.

Skills acquired

  • Data and information retrieval, analysis and synthesis
  • Decision making
  • Individual work
  • Team work
  • New research ideas generation
  • Promotion of free, creative and inductive thinking

 

  • Introduction to Artificial Intelligence, historic overview
  • Blind search algorithms
  • Heuristic search algorithms
  • Game trees
  • Constraint Satisfaction Programming
  • Propositional Logic
  • First-order Logic
  • Reasoning
  • Knowledge-based systems, expert systems
  • Autonomous agents
  • Applications
  1. Artificial Intelligence: A Modern Approach, Russel & Norvig, 4th Edition.
  2. Superintelligence: Paths, Dangers, Strategies, N. Bostrom 2014.

Related scientific journals:

  • Artificial Intelligence
  • IEEE Transactions on Artificial Intelligence
  • AAAI Conference Proceedings
Learning Results - Skills acquired

Learning Results

Upon successful completion of the course, students will be able to:

  • Gain a historical perspective of AI and its foundations.
  • Become familiar with basic principles of AI toward problem solving, inference, perception, knowledge representation, and learning.
  • Investigate applications of AI techniques in intelligent agents, expert systems, etc.
  • Explore the current scope, potential, limitations, and implications of intelligent systems.

Skills acquired

  • Data and information retrieval, analysis and synthesis
  • Decision making
  • Individual work
  • Team work
  • New research ideas generation
  • Promotion of free, creative and inductive thinking

 

Course content
  • Introduction to Artificial Intelligence, historic overview
  • Blind search algorithms
  • Heuristic search algorithms
  • Game trees
  • Constraint Satisfaction Programming
  • Propositional Logic
  • First-order Logic
  • Reasoning
  • Knowledge-based systems, expert systems
  • Autonomous agents
  • Applications
Recommended bibliography
  1. Artificial Intelligence: A Modern Approach, Russel & Norvig, 4th Edition.
  2. Superintelligence: Paths, Dangers, Strategies, N. Bostrom 2014.

Related scientific journals:

  • Artificial Intelligence
  • IEEE Transactions on Artificial Intelligence
  • AAAI Conference Proceedings