1) Introduction to machine learning, mathematical background, optimization in machine learning.
2) Model evaluation - supervised pipeline, validation approaches, metrics, preprocessing.
3) Dimensionality reduction - feature selection and feature reduction methods.
4) Linear models - definition, losses and regularizations, classification models, regression models.
5) Bias-variance tradeoff, decision trees and random forests, bagging and boosting.
6) Basics of neural networks - simple neural network, activation functions, loss functions, regularization, optimization methods.
7) Principles of deep learning - deep neural networks (NN) and basic building blocks.
8) Principles of deep NN - special blocks.
9) Architectures and applications of deep NN - regression, classification, image2image and signal2signal.
10) Transformers - attention mechanism, tokenizations, vision transformers, language models.
11) Probabilistic models basics - probability distributions, maximum likelihood estimation, maximum a-posteriori estimation.
12) Probabilistic models - Naive Bayes Classifier, Gaussian mixture model, Logistic regression. 

During computer exercises, students practically implement machine learning algorithms in Python using scikit-learn, PyTorch, and other libraries. Exercise topics are as follows: 

1) Introduction to machine learning in Python, simple classifier example, introduction to scikit-learn.
2) Model evaluation methods – metrics, model validation.
3) Dimensionality reduction - introduction to pandas, feature selection and PCA.
4) Linear models - linear and polynomial regression, LASSO/RIDGE regression, classification with linear models.
5) Dual forms and kernels - regression, SVM.
6) Decision trees, random forest and boosting.
7) Artificial neural network introduction, introduction to PyTorch.
8) Practical deep learning example - image classification deployed on smartphone.
9) Deep learning in various applications – image classification, signal classification, image segmentation, image2image regression, signal segmentation, 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, Logistic regression. 

Throughout the semester, students complete assigned tasks and projects focused on the practical implementation of machine learning algorithms. Tasks include programming in Python, data analysis, model training, and performance evaluation. Students independently implement solutions based on knowledge acquired from lectures and exercises, with the possibility of consulting with the instructor.