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
Number of ECTS credits
Mode of study
Guarantor
Offered to foreign students
Entry knowledge
Rules for evaluation and completion of the course
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
Study aids
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
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
Teacher / Lecturer
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