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BECM33MLF	Machine Learning Fundamentals
Role:	P, PV, PS	Rozsah výuky:	2P+2C
Katedra:	13133	Jazyk výuky:	EN
Garanti:	Franc V.	Zakončení:	Z,ZK
Přednášející:	Franc V.	Kreditů:	6
Cvičící:	Franc V., Paplhám J.	Semestr:	L,Z

Webová stránka:

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

Anotace:

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.

Cíle studia:

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.

Osnovy přednášek:

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.

Osnovy cvičení:

Literatura:

- 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

Požadavky:

Předmět je zahrnut do těchto studijních plánů:

Plán	Obor	Role	Dop. semestr
MPKYR_2021	Před zařazením do oboru	PV	1,3
MPOI5_2026	Počítačové vidění	PS	1
MEBIO2_2018	Medical Instrumentation	PV	1
MEBIO4_2018	Signal Processing	PV	1
MPBIO3_2018	Zpracování obrazu	PS	1
MPBIO4_2018	Zpracování signálů	PV	1
MPPRGAI_2025	Před zařazením do oboru	P	1
MPBIO2_2018	Lékařská technika	PV	1
MEKYR_2021	Před zařazením do oboru	PV	1,3
MPOI7_2026	Umělá inteligence	PS	1
MEBIO3_2018	Image Processing	PS	1
MEBIO1_2018	Bioinformatics	PS	1
MPBIO1_2018	Bioinformatika	PS	1

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Stránka vytvořena 19.5.2026 14:51:53, semestry: Z/2028-9, Z/2026-7, Z,L/2025-6, L/2026-7, L/2028-9, L/2027-8, L/2029-30, Z/2027-8, připomínky k informační náplni zasílejte správci studijních plánů

Návrh a realizace: I. Halaška (K336), J. Novák (K336)