Course detail
Machine Learning
FEKT-MPA-MLRAcad. year: 2021/2022
Students will gain insight into advanced machine learning methods. They will be able to describe and compare the properties of individual approaches to data classification. They will be able to select and apply a specific approach to a given problem. They will also gain practical experience with current implementations of machine learning methods including deep learning.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Offered to foreign students
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
There will be 6 tests during the semester (each test for maximum of 5 points). The tests cannot be repeated.
The conditions for the award of credit are as follows:
- full participation in the computer labs (max. two excused absences),
- obtaining at least 15 points from the tests.
Obtaining credit is a condition for admission to the final examination.
The final exam will be marked with a maximum of 70 points. A minimum of 35 points is required to pass the exam.
Course curriculum
2. Features assessment, feature selection and feature reduction with basic and advanced methods (PCA, mRMR, t-SNE).
3. Linear Classifiers - basic principles and methods (perceptron, SVM, MSE).
4. Kernel approach for non-linear classification.
5. Bayesian approach to classification. Naive Bayes classifier.
6. Maximum likelihood and Maximum a-posteriori probability.
7. Decision and regression trees and forests, random forests.
8. Methods for improving classifier properties (bagging, boosting).
9. Basics of neural networks, regularization.
10. Principles of deep learning, deep neural networks (NN) and basic building blocks.
11. Principles of deep NN learning.
12. Variants of deep NN, autoencoders, recurrent NN, LSTM, GRU, GAN.
13. Application of classification tasks for processing of signals, images and bioinformatic data. Application examples.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Geron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2. edition, O'Reilly Media (EN)
Ch. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2011
I. Goodfellow, Y. Bengio, A. Courville, F. Bach: Deep Learning, The MIT Press, 2016
N. Buduma: Fundamentals of Deep Learning, O'Reilly Media, 2017 (CS)
Recommended reading
Elearning
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Exercise in computer lab
Teacher / Lecturer
Elearning