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
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.
Knowledge and understanding:
Providing a wide overview of the main machine learning methods and
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.
Ability of evaluating performance of a machine learning system using
proper metrics and evaluation methodologies.
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.
Self-study of specific application domains, problems and solutions
during the homeworks, with possible application of teamwork for the
solution of specific problems.
FABIO PATRIZI Teacher profile
Basic concepts and evaluation
Support Vector Machines
Linear and logistic regression
Instance based (K-NN)
Deep neural networks (CNN)
Latent variables (EM)
Deep Reinforcement Learning
Machine Learning, Tom Mitchell.
Pattern Recognition and Machine Learning, Chris Bishop.
Machine Learning: a Probabilistic Perspective, Kevin Murphy.
Introduction to Machine Learning, Ethem Alpaydin.
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville.
Reinforcement Learning, Sutton and Barto.
Basic knowledfge of programming languages, algorithms, data structures, probability and statistics.
The course consists in theretical-practical lectures.
The exam consists in a written test.
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VALSAMIS NTOUSKOS Teacher profile
- Academic year: 2019/2020
- Curriculum: Ingegneria Informatica (percorso valido anche ai fini del conseguimento del doppio titolo italo-francese, italo-venezuelano o italo-russo)
- Year: First year
- Semester: First semester
- SSD: ING-INF/05
- CFU: 6
- Attività formative caratterizzanti
- Ambito disciplinare: Ingegneria informatica
- Exercise (Hours): 36
- Lecture (Hours): 24
- CFU: 6.00
- SSD: ING-INF/05