Subject description - BE4M33SSU

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BE4M33SSU Statistical Machine Learning
Roles:PS, PO, PV Extent of teaching:2P+2C
Department:13133 Language of teaching:EN
Guarantors:Flach B. Completion:Z,ZK
Lecturers:Drchal J., Flach B., Franc V. Credits:6
Tutors:Drchal J., Flach B., Franc V., Paplhám J. Semester:Z

Web page:

https://cw.fel.cvut.cz/wiki/courses/BE4M33SSU

Anotation:

The aim of statistical machine learning is to develop systems (models and algorithms) for learning to solve tasks given a set of examples and some prior knowledge about the task. This includes typical tasks in speech and image recognition. The course has the following two main objectives
1. to present fundamental learning concepts such as risk minimisation, maximum likelihood estimation and Bayesian learning including their theoretical aspects,
2. to consider important state-of-the-art models for classification and regression and to show how they can be learned by those concepts.

Study targets:

The aim of statistical machine learning is to develop systems (models and algorithms) for learning to solve tasks given a set of examples and some prior knowledge about the task.

Course outlines:

The course will cover the following topics - Empirical risk minimization, consistency, bounds - Maximum Likelihood estimators and their properties - Unsupervised learning, EM algorithm, mixture models - Bayesian learning - Deep (convolutional) networks - Supervised learning for deep networks - Hidden Markov models - Structured output SVMs - Ensemble learning, random forests

Exercises outline:

Labs will be dedicated to practical implementations of selected methods discussed in the course as well as seminar classes with task-oriented assignments.

Literature:

1. M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012
2. K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
3. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010
4. I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016

Requirements:

Prerequisites of the course are: - foundations of probability theory and statistics comparable to the scope of the course "Probability, statistics and information theory" (A0B01PSI), - knowledge of statistical decision theory foundations, canonical and advanced classifiers as well as basics of machine learning comparable to the scope of the course "Pattern Recognition and Machine Learning" (AE4B33RPZ)

Keywords:

machine learing, statistical learning

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEBIO3_2018 Image Processing PS 1
MEBIO1_2018 Bioinformatics PS 1
MPOI8_2018 Bioinformatics PO 3
MEKYR_2021 Common courses PV 1,3
MPBIO3_2018 Image processing PS 1
MPBIO1_2018 Bioinformatics PS 1
MEOI7_2018 Artificial Intelligence PO 1
MPOI5_2018 Computer Vision and Image Processing PO 1
MEBIO2_2018 Medical Instrumentation PV 1
MEBIO4_2018 Signal Processing PV 1
MPOI9_2018 Data Science PO 3
MEOI9_2018 Data Science PO 3
MEOI8_2018 Bioinformatics PO 3
MPBIO2_2018 Medical Instrumentation PV 1
MPOI7_2018 Artificial Intelligence PO 1
MPBIO4_2018 Signal processing PV 1
MPKYR_2021 Common courses PV 1,3
MEOI5_2018 Computer Vision and Image Processing PO 1


Page updated 11.12.2023 15:51:36, semester: Z/2024-5, L/2023-4, L/2022-3, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)