Course detail

Machine Learning

FEKT-MPA-MLRAcad. year: 2023/2024

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

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Tomáš Vičar, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Offered to foreign students

The home faculty only

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

A maximum of 30 points can be obtained during the semester. A maximum of 70 points can be obtained in the final examination.

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.

Aims

Students will gain an overview in the field of advanced machine learning methods. They will be able to describe and compare the characteristics of various approaches to data classification. They will also be capable of selecting and applying a specific approach to a given problem. Additionally, they will acquire practical experience with current implementations of machine learning methods, including deep learning. 

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Deisenroth,M.P, Faisal, A.A, Ong, Ch.S.:Mathematics for Machine Learning, Cambridge University Press, 2020 (EN)
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

Not applicable.

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MPC-BTB Master's 2 year of study, winter semester, compulsory
  • Programme MPC-BIO Master's 2 year of study, winter semester, compulsory
  • Programme MPAD-BIO Master's 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Tomáš Vičar, Ph.D.
doc. Ing. Radim Kolář, Ph.D.
Ing. Jiří Chmelík, Ph.D.

Syllabus

1. Introduction to classification. Classification error, classifier testing.
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. 

 

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Tomáš Vičar, Ph.D.

Syllabus

1) Introduction to machine learning in pyton, simple classifier example
2) Model evaluation methods – metrics, model validation
3) Linear and polinomial regression, LASSO/RIDGE regression
4) Dual forms and kernels - regression, SVM
5) Feature selection - feature filtering, feature wrapping, forward method
6) Decision trees, random forest, bias-variance trade-off
7) Artificial neural network introduction, introduction to PyTorch
8) Introduction to PyTorch 2, simple neural network
9) Deep learning in various applications – image classification, signal classification, image segmentation, image2image regression, signal segmentation, signal2signal regression, signal2signal regression
10) Transformers - vision transformer example, next word prediction example
11) Probabilistic models 1 - Maximum likelihood estimation, Maximum a-posteriori estimation
12) Probabilistic models 2 - Naive Bayes Classifier, Gaussian mixture model 

Elearning

eLearning: currently opened course