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BECM33MLF	Machine Learning Fundamentals
Roles:	PV, P, PS	Extent of teaching:	2P+2C
Department:	13133	Language of teaching:	EN
Guarantors:	Franc V.	Completion:	Z,ZK
Lecturers:	Franc V.	Credits:	6
Tutors:	Franc V., Paplhám J.	Semester:	L,Z

Web page:

https://cw.fel.cvut.cz/wiki/courses/becm33mlf/start

Anotation:

The aim of this course is to provide a comprehensive understanding of the fundamental principles underlying machine learning algorithms and to explain their use in basic machine learning algorithms. The goal of statistical machine learning is to design systems incorporating models and algorithms capable of learning to solve problems based on the examples provided and prior knowledge of the problem. This course is designed with two main objectives. First, it seeks to clarify the basic principles of learning, such as risk minimization, maximum likelihood learning, and Bayesian learning, and to delve into their theoretical foundations. Second, it seeks to explore the basic models for classification and regression and show how these models can be effectively learned by applying these basic concepts.

Study targets:

The aim of this course is to provide a comprehensive understanding of the fundamental principles underlying machine learning algorithms and to explain their use in basic machine learning algorithms. The goal of statistical machine learning is to design systems incorporating models and algorithms capable of learning to solve problems based on the examples provided and prior knowledge of the problem. This course is designed with two main objectives. First, it seeks to clarify the basic principles of learning, such as risk minimization, maximum likelihood learning, and Bayesian learning, and to delve into their theoretical foundations. Second, it seeks to explore the basic models for classification and regression and show how these models can be effectively learned by applying these basic concepts.

Course outlines:

1.		Gentle introduction to generalization.
2.		Empirical Risk Minimization. Statistical preliminaries (convergence of RVs, the law of large numbers, Hoeffding inequality).
3.		Generalization theory. PAC learning.
4.		VC dimension.
5.		Bias-Variance trade-off.
6.		Model selection and validation methods.
7.		Performance metrics.
8.		Linear models. Perceptron algorithm
9.		Support Vector Machines.
10.		Kernel methods.
11.		Deep learning and generalization.
12.		Generative learning.
13.		Bayesian learning.

Exercises outline:

Literature:

- T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 - M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 - Y.S. Abu-Mostafa, M.M. Ismail, H.T. Lin. Learning from data. AMLBook, 2012. - I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016

Requirements:

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MEBIO3_2018	Image Processing	PS	1
MEBIO1_2018	Bioinformatics	PS	1
MEBIO2_2018	Medical Instrumentation	PV	1
MPKYR_2021	Common courses	PV	1,3
MPBIO1_2018	Bioinformatics	PS	1
MPBIO3_2018	Image processing	PS	1
MPBIO4_2018	Signal processing	PV	1
MPPRGAI_2025	Common courses	P	1
MEBIO4_2018	Signal Processing	PV	1
MPBIO2_2018	Medical Instrumentation	PV	1
MEKYR_2021	Common courses	PV	1,3

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Page updated 19.4.2026 17:51:07, semester: L/2025-6, L/2029-30, Z/2028-9, Z/2025-6, L/2028-9, Z/2026-7, Z,L/2027-8, L/2026-7, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)