Course Details

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Course Type (English)

Prerequisites (English) Computational Mathematics Discrete Mathematics Probability Theory Numerical Analysis Programming I & II Object Oriented Programming I & II Data Structures Specific topics: Linear algebra: Geometry and algebra of vectors, systems of linear equations, matrices and linear transformations, diagonalization and eigenvectors Multivariate calculus: vector valued functions and functions of several variables, parametric curves, partial derivatives and gradients, the derivative as a matrix, chain rule. Probability: Counting, axioms of probability, conditioning and independence, expectation and variance, discrete and continuous random variables and distributions, joint distributions and dependence, central limit theorem and laws of large numbers.

Course URL (e.g., on e-class)

Learning Outcomes (English) The course aims at introducing students to fundamental concepts of artificial intelligence. The course first focuses on problem solving by searching, and particularly to uniformed and informed search methods. Moreover, the course teaches techniques for solving Constrained Satisfaction Problems, as well as methods for maximizing the expected utility of agents in adversarial environments (adversarial search methods). Finally, the course offers an introduction to Machine Learning, with particular emphasis on principles of generalization of machine learning models, and an introduction linear models, including Linear Regression and Logistic Regression models. After successfully completing this course, students should be able to understand and apply appropriate methods from each category to real-world Artificial Intelligence problems.

General Competencies (English) - Adaptation in new conditions - Independent work - Team work - Decision making - Promoting free, creative and deductive reasoning

Course Content (English) - Introduction to Artificial Intelligence - State space represenations and solving problems by searching - Uninformed search algorithms: DFS, BFS, UCS and iterative deepning - Informed search algorithms: Greedy and A* search - Adversarial search. Minimax, Expectimax and their variants - Agent utility in search problems - Constraint Satisfaction Problems - Backtracking search with arc-consistency - Local search algorithms and their variants - Introduction to machine learning and generalization - Ordinary least squares linear regression - Logistic regression and linear classifiers - Brief introduction to artificial neural networks and representation learning

Use of ICT (English) eclass course web page Course projects in Python

Is it elective? Άγνωστο Ναι Όχι

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Independent Study*

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