Catalogo dei Corsi di studio

General Objectives:

The objectives of this course are to present a wide spectrum of Machine
Learning methods and algorithms, discuss their properties, convergence
criteria and applicability. The course will also present examples of
successful application of Machine Learning algorithms in different
application scenarios.
The main outcome of the course is the capability of the students of
solving learning problems, by a proper formulation of the problem, a
proper choice of the algorithm suitable to solve the problem and the
execution of experimental analysis to evaluate the results obtained.

Specific Objectives:

Knowledge and understanding:
Providing a wide overview of the main machine learning methods and
algorithms
for the classification, regression, unsupervised learning and
reinforcement learning problems. All the problems are formally defined
and theoretical basis as well as technical and implementation details
are provided in order to understand the proposed solutions.

Applying knowledge and understanding:
Solving specific machine learning problems starting from training data,
through a proper application of the studied methods and algorithms. The
development of two homeworks (small projects to be developed at home)
allows the students to apply the acquired knowledge.


Making judgements:
Ability of evaluating performance of a machine learning system using
proper metrics and evaluation methodologies.

Communication skills:
Ability of writing a technical report describing the results of the
homeworks, thus showing abilities in communicating results obtained from
the application of the acquired knowledge in solving a specific problem.
Being exposed to examples of communication of results obtained in
practical cases given by experts within seminars offered during the course.

Learning skills:
Self-study of specific application domains, problems and solutions
during the homeworks, with possible application of teamwork for the
solution of specific problems.

Programme

Classification
- Basic concepts and evaluation
- Decision Trees
- Bayes Learning
- Linear Models
- Support Vector Machines
- Kernels
- Multiple classifiers
- Regression
- Linear and logistic regression
- Instance based (K-NN)
- Perceptron
- Neural networks
- Deep neural networks (CNN)
Unsupervised learning
- Clustering (k-Means)
- Latent variables (EM)
Reinforcement learning
- MDP
- Q-learning

Adopted texts

Teaching material distributed during the course

Other suggested textbooks:
- Machine Learning, Tom Mitchell.
- Pattern Recognition and Machine Learning, Chris Bishop
- Machine Learning: a Probabilistic Perspective, Kevin Murphy
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville

Prerequisites

Basic notions on mathematics Basic knowledge on probabilities and statistics Basic knowledge on algorithms and data structures Basic knowledge on search problems

Study modes

In-presence lectures

Frequency modes

Attending in-presence lectures is strongly suggested

Exam modes

Written test