20801730 - ARTIFICIAL INTELLIGENCE

Introduce the fundamental techniques of the various areas of Artificial Intelligence relative to Knowledge Representation and Automatic Reasoning, Machine Learning, Natural Language processing, Computer Vision.
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Programme

1. Introduction:

- Intelligent Agents.
- AI as representation and search.

2. Solving problems by Searching:

-Blind search (Breadth-first search, Uniform cost search, Depth-first search, Iterative deepening search).
- Heuristic search (Best First, A*, IDA*, Heuristic Functions).
- Approximate Algoritms (Hill Climbing, Simulated Annealing, etc.)
- Two-Person games (MiniMax, Alfa-Beta Pruning).

3. Knowledge Representation and Automated reasoning:

- Frames, Semantic Networks, Production Systems.
- Case-Based Reasoning.
- Knowledge Based Systems.

4. Machine Learning:

- Symbol-Based (Inductive Learning, Decision trees).
- Connectionist (Artificial Neural Networks).

5. Communicating, Perceiving and Acting:

- Natural language Processing and Information retrieval.
- Computer Vision.



Core Documentation

S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 3/Ed (2010). Pearson Education.

Lecture notes by the professor.

Type of delivery of the course

The procedure consists of lectures and classroom exercises.

Type of evaluation

Verification of learning takes place through a written test. The test is organized through a number of questions, aimed at verifying the level of understanding of the concepts and methods presented in the course.