Subject description - BE4M33SSU
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BE4M33SSU | Statistical Machine Learning | ||
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Roles: | PS, PO, PV | Extent of teaching: | 2P+2C |
Department: | 13133 | Language of teaching: | EN |
Guarantors: | Franc V. | Completion: | Z,ZK |
Lecturers: | Drchal J., Franc V. | Credits: | 6 |
Tutors: | Drchal J., Franc V., Paplhám J. | Semester: | Z |
Web page:
https://cw.fel.cvut.cz/wiki/courses/BE4M33SSUAnotation:
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 objectives1. | 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 forestsExercises 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:Page updated 14.10.2024 17:51:34, semester: Z/2025-6, Z,L/2024-5, L/2023-4, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |