Teaching

CS-487 (Introduction to Artificial Intelligence)

Fall 23-24, 24-25

Textbook

  • Russell, S. J., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach 4th Edition. Pearson Education.

Syllabus

  1. Introduction to AI
    • Foundations of AI, History of AI, State of the Art, Risks and Benefits
  2. Intelligent Agents
    • Agents and Environments, Rationality, Agent Structures
  3. Solving Problems by Searching
    • Problem-Solving Agents, State-space search, problem formulation, Search Algorithms: Uninformed (DFS, BFS, uniform-cost) and informed (\(A^*\), greedy search) search strategies, Heuristic Functions
  4. Local Search and Optimization
    • Local Search Algorithms (hill-climbing, simulated annealing, genetic algorithms), Continuous Space Optimization, Partially Observable Environments, Online Search Agents
  5. Constraint Satisfaction Problems (CSPs)
    • Constraint Propagation: Inference techniques, Backtracking and Local Search
  6. Adversarial Search
    • Minimax and alpha-beta pruning, Monte Carlo Tree Search, Stochastic and Partially Observable Games
  7. Logical Agents
    • Knowledge-Based Agents, Propositional Logic for reasoning, Theorem Proving, Model Checking
  8. First-Order Logic (FOL)
    • Syntax and Semantics of FOL, Knowledge Engineering, Inference in FOL (unification, forward/backward chaining, resolution rule)
  9. Knowledge Representation
    • Ontological Engineering, Categories and Objects, Reasoning Systems
  10. AI Planning
    • Classical Planning, Planning Heuristics, Hierarchical Planning, Nondeterministic Planning, Resource Planning

Prerequisites

  • CS-240 (Data Structures)
  • CS-180 (Logic)

Course material is available on Moodle (e-learn platform).

CS-119 (Linear Algebra)

Spring 22-23, 23-24

Textbook

  • Strang, G. (2022). Introduction to Linear Algebra. Wellesley-Cambridge Press.

Syllabus

  1. Matrices and Gaussian Elimination
    • Matrix Algebra, Vector Spaces and Subspaces, Linear System Solving, Linear Independence, Basis, and Dimension, The Four Fundamental Subspaces, Linear Transformations and Mappings
  2. Orthogonality
    • Orthogonal Vectors and Subspaces, Cosines and Projections onto Lines, Projections and Least Squares, Orthogonal Bases and Gram-Schmid, orthogonality theorems
  3. Determinants
    • Properties, formulas and applications
  4. Eigenvalues and Eigenvectors
    • Eigenvalues and eigenvectors, Diagonalization of a Matrix, using eigenvalues to solve difference equations, Solving differential equations using eigenvalues, Complex Matrices, Similarity Transformations

Course material is available on Moodle (e-learn platform).

CS-100 (Introduction to Computer Science)

Fall 22-23

Textbook

  • Kernighan, B. W., & Ritchie, D. M. (2002). The C programming language. Prentice-Hall.

Syllabus

  1. Introduction, Algorithms and Programs, Types, Operators, I/O, Control Flow Instructions, Loop Instructions, Functions, Functions and Variables, Arrays, Strings, Multidimensional Arrays, Pointers, Dynamic Memory Management, File Management, Recursion, Sorting and Searching, Structure Types, Recursive Structures, Simply Linked Lists, Doubly Linked Lists, Other Properties of C, Advanced Programming Techniques.

Course material is available on the course’s webpage.