2-INF-150, Zima 2025 / Fall 2025: Basic information

2-INF-150: Strojové učenie / Machine Learning
Zima 2025 / Fall 2025
Basic information

Syllabus

Supervised machine learning (linear and generalized linear regression, neural networks, classification with support vector machines, kernel methods, discrete classifiers). Machine learning theory (statistical model of machine learning, bias-variance trade-off, overfitting and underfitting, PAC learning, VC dimension estimates). Unsupervised machine learning (clustering, self-organizing maps, principal component analysis). Reinforcement learning. Ensemble learning (bagging, boosting). Introduction to modern AI methods (deep neural networks, LLMs).

Recommended literature

The lectures will be loosely inspired by the recommended literature and will cover only some chapters. Lectures will be the primary source of information for the exam.

[GBC] Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press 2016
in library: I-INF-G-57
[HTF] Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning, 2nd ed. Springer 2009
in library: I-INF-H-10
[B] Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer 2006
in library: I-INF-B-38

Grading

Theoretical homeworks (2) 10%

Coding homeworks (4) 12%

Tutorials 5% each tutorial

Project 30%

Written exam 35%

Oral exam about project 5%

Note that the number of points that can be obtained sum up to more than 100%. The rest are bonus points.

Final grade: A: 90+, B: 80+, C: 70+, D: 60+, E: 50+
To receive the final grade, you have to receive at least 50% points on the exam and attend oral exam if required by the lecturer.

Project instructions: here
Basic information: The deadline for projects is 6th January 2026. By 13th January you will received feedback, with possible avenues for improvement. 27th January is deadline for improvements. Oral exams about project will follow after that.

All homeworks solutions must be your own work. It is not permitted to search internet and literature for homework solutions.

Complaints about grades

All complaints about homework grades must be submitted in writing within two weeks of the time when the graded solutions are available, but no later than on the business day before the exam date. By examining your solution in more detail, the grade can be increased or decreased. Before submitting a claim, carefully read the relevant materials.

Academic Integrity

Copying homework assignments, projects and cheating on exams is a serious violation of academic integrity.

Cheating on homework assignments and projects include copying work of somebody else (i.e. classmate, internet, literature) and handing the work under your name, allowing somebody else to copy your work, or excessive collaboration. Cheating on exam includes use of unauthorized devices, as well as communication with others during the exam.

The standard penalty for cheating on homework assignment is a grade of -100%. Penalty for cheating on exam is Fx with no option of retaking the exam. Serious infraction will be reported to the faculty disciplinary committee.

With respect to homeworks, we encourage discussion between students and in groups. However, the solution you hand in must be your own and described in your own words. To avoid problems, do not keep any notes from such discussions and wait for several hours after the discussion before writing up your own solution.

Maintained by 2-INF-150 personnel