The goal is to present the fundamental models, methods and techniques of some relevant areas of Artificial Intelligence, with particular reference to heuristic search and Machine Learning, and to use them as tools for the development of innovative technologies. As for Machine Learning, the course will allow students to learn the main methods and algorithms typical of the discipline (supervised, unsupervised and with reinforcement). 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. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Lecture notes by the professor.
Mutuazione: 20810322 Intelligenza artificiale e machine learning in Ingegneria gestionale e dell'automazione LM-32 SANSONETTI GIUSEPPE, MICARELLI ALESSANDRO, SANZARI MARTA
Programme
1. Introduction:- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Core Documentation
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2021). Pearson Education.Lecture notes by the professor.
Type of delivery of the course
In-person classes and in-class labs (except for periods of sanitary emergency).Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written exam and practical laboratory test. teacher profile teaching materials
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem-Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Lecture notes by the professor.
Mutuazione: 20810322 Intelligenza artificiale e machine learning in Ingegneria gestionale e dell'automazione LM-32 SANSONETTI GIUSEPPE, MICARELLI ALESSANDRO, SANZARI MARTA
Programme
1. Introduction:- Intelligent Agents.
- AI as "Representation and Search".
2. Problem-Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Core Documentation
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2021). Pearson Education.Lecture notes by the professor.
Type of delivery of the course
Traditional. During the COVID-19 emergency period, the exam will be carried out in accordance with the provisions of Article 1 of the Rector's Decree no. 703 of 5 May 2020.Type of evaluation
Written exam and practical laboratory test.Mutuazione: 20810322 Intelligenza artificiale e machine learning in Ingegneria gestionale e dell'automazione LM-32 SANSONETTI GIUSEPPE, MICARELLI ALESSANDRO, SANZARI MARTA
teacher profile teaching materials
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Lecture notes by the professor.
Programme
1. Introduction:- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Core Documentation
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2021). Pearson Education.Lecture notes by the professor.
Type of delivery of the course
In-person classes and in-class labs (except for periods of sanitary emergency).Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written exam and practical laboratory test. teacher profile teaching materials
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem-Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Lecture notes by the professor.
Programme
1. Introduction:- Intelligent Agents.
- AI as "Representation and Search".
2. Problem-Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.
Core Documentation
S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 4/Ed (2021). Pearson Education.Lecture notes by the professor.
Type of delivery of the course
Traditional. During the COVID-19 emergency period, the exam will be carried out in accordance with the provisions of Article 1 of the Rector's Decree no. 703 of 5 May 2020.Type of evaluation
Written exam and practical laboratory test.