A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
NI-AML Advanced machine learning Extent of teaching: 2P + 1C Instructor: Buk Z., Da Silva Alves R., Čepek M., Šimánek P., Rybář V. Completion: Z,ZK Department: 18105 Credits: 5 Semester: L Annotation:
The course introduces students to selected advanced topics of machine learning and artificial intelligence. The topics present techniques in the field of recommendation systems, image processing, control and interconnection of physical laws with the field of machine learning. The aim of the exercise is to familiarize students with the methods discussed.
Lecture syllabus:
1. Introduction, Repeatable ML Projects - MLOps 2. Optimisation in Deep Learning 3. Recommender Systems 4. Recommender Systems 5. Continual Learning 6. ML in modeling and control 7. Advanced Image Processing 8. Physics informed ML 9. Interpretable and Explainable Models 10. Causal Machine Learning 11. Time Series Modeling 12. AI Alignment Seminar syllabus:
1. Optimisation in Deep Learning 2. Recommender Systems 3. ML in modeling and control 4. Physics informed ML 5. Interpretable and Explainable Models 6. Semestral project presentation Literature:
[1] Silva, N., Werneck, H., Silva, T., Pereira, A. C., & Rocha, L. (2022). Multi-Armed Bandits in Recommendation Systems: A survey of the state-of-the-art and future directions. Expert Systems with Applications [2] McAuley, J. (2022). Personalized Machine Learning. Cambridge University Press. [3] Gift, N., & Deza, A. (2021). Practical MLOps. " O'Reilly Media, Inc.". [4] Rajendra, P., Ravi. PVN, H., & Naidu T, G. (2021). Optimization methods for deep neural networks. In AIP Conference Proceedings (Vol. 2375, No. 1, p. 020034). AIP Publishing LLC. [5] Bagus, B., Gepperth, A., & Lesort, T. (2022). Beyond Supervised Continual Learning: a Review. [6] Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422-440. [7] Kirchner, J. H., Smith, L., Thibodeau, J., McDonell, K., & Reynolds, L. (2022). Researching Alignment Research: Unsupervised Analysis. [8] Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society [9] Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: a review. Data mining and knowledge discovery Requirements:
Recommended prerequisite is "NI-MVI Computational Inteligence Methods" course. We assume knowledge of forward, convolution neural networks, autoencoders, transformers.
Informace o předmětu a výukové materiály naleznete na http://courses.fit.cvut.cz/NI-AML The course is also part of the following Study plans:
Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška