The goal is to present the fundamental models, methods and techniques of various areas of Artificial Intelligence, with particular reference to heuristic search, knowledge representation and automatic reasoning, machine learning, natural language processing, computer vision. The lessons and practical exercises carried out during the course will allow the student to acquire analytical and problem solving skills on various domains of interest for the discipline.
Curriculum
teacher profile teaching materials
- 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.
Lecture notes by the professor.
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
TraditionalType of evaluation
Written test teacher profile teaching materials
- 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.
Lecture notes by the professor.
Mutuazione: 20801730 INTELLIGENZA ARTIFICIALE in Ingegneria informatica LM-32 N0 MICARELLI ALESSANDRO
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
TraditionalType of evaluation
Written test teacher profile teaching materials
- 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.
Lecture notes by the professor.
Mutuazione: 20801730 INTELLIGENZA ARTIFICIALE in Ingegneria informatica LM-32 N0 MICARELLI ALESSANDRO
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
TraditionalType of evaluation
Written test teacher profile teaching materials
- 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.
Lecture notes by the professor.
Mutuazione: 20801730 INTELLIGENZA ARTIFICIALE in Ingegneria informatica LM-32 N0 MICARELLI ALESSANDRO
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
TraditionalType of evaluation
Written test