Lectures are an essential part of our course. Here you can get lecture notes and course materials. Lectures and labs are typically scheduled on Thursday mornings between 9:30 and 12:30 and are held in the Institute of Information Theory and Automation (UTIA), room 25. If you have any questions please contact 
Exams
Exam dates: 13.1. and 20.1.
Place&time: UTIA, room 25, 9:30am
Lectures schedule
Lecture 1 – Introduction to Machine Learning 1/3 – 30.9.2021
Introduction to USU and SU2, what is AI, ML, handcrafted features versus learned features, supervised versus unsupervised learning, AI as an optimization problem
Lecture 2 – Introduction to Machine Learning 2/3 – 7.10.2021
Bayesian classifier, K-NN classifier, SVM, k-means clustering, PCA
Lecture 3 – Introduction to Machine Learning 3/3, Introduction to Deep Learning – 14.10.2021
Lecture 4/Lab – Decision trees – 4.11.2021
Random forest, AdaBoost, XgBoost
The second half of the lecture will be practical with hands-on examples.
Lecture 5 – Neural Networks – 11.11.2021
NN theory, output units, hidden units, activation functions
Lecture 6 – Optimization, CNN – 25.11.2021
stochastic optimization (SGD, ADAM,…), CNN building blocks
Lecture 7 – Advanced Topics – 9.12.2021
regularization, examples of CNN architecture, GAN, VAE, RNN, Transformer
Handouts
Lectures 1-3: Introduction to Machine Learning
Lecture 3b: Introduction to Deep Learning
Lecture 5: Theory and Basic Building Blocks
Lecture 6: Optimization and CNN architecture
Lecture 7: Advanced Topics