Code: BE4M33SSU Statistical Machine Learning
Lecturer: Ing. Vojtìch Franc Ph.D. Weekly load: 2P+2C Completion: A, EX
Department: 13133 Credits: 6 Semester: W
Description:
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.
Contents:
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
Seminar contents:
Labs will be dedicated to practical implementations of selected methods discussed in the course as well as seminar classes with task-oriented assignments.
Recommended 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
Keywords:
machine learing, statistical learning

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