The course will allow students to gain insight into fundamental methods, techniques, and algorithms in various areas of Artificial Intelligence and Machine Learning. Specific references will be made to autonomous search and knowledge representation. In the area of Machine Learning, the focus will be on regression, classification and clustering techniques. Finally, the principles of deep neural networks (deep learning) will be introduced. In addition to lectures, the course includes practical exercises that will allow the student to acquire analysis and problem solving skills on various domains of interest related to the degree program.
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.
Fruizione: 20810322 Intelligenza artificiale e machine learning in Ingegneria gestionale e dell'automazione LM-32 SANSONETTI GIUSEPPE, MICARELLI ALESSANDRO
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
Lecture slides.Type of delivery of the course
In-person classes and in-class labs.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.
Fruizione: 20810322 Intelligenza artificiale e machine learning in Ingegneria gestionale e dell'automazione LM-32 SANSONETTI GIUSEPPE, MICARELLI ALESSANDRO
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
Lecture slides.Type of delivery of the course
In-person classes and in-class labs.Attendance
Attendance is not compulsory, but it is strongly recommended.Type of evaluation
Written exam and practical laboratory test.