13M051MU - Machine Learning
Course specification | ||||
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Course title | Machine Learning | |||
Acronym | 13M051MU | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | Introduce students to theoretical and practical aspects of supervised, self-supervised and unsupervised machine learning. illustration of various application areas, with guidelines on how to choose the adequate model, and how to optimize, evaluate and implement it. | |||
The outcome | Students will be able to: choose an adequate machine learning algorithm suited to real-world problems, implement it, optimize its parameters and evaluate its performance. Special attention will be paid to techniques for formulating the problem and casting it in a configuration best suited to the application of the methods covered in this course. | |||
Contents | ||||
Contents of lectures | Linear and logistic regression. Numerical optimization methods. Exponential family of distributions and generalized linear models. Generative algorithms. Support vector machines. Decision trees. Bagging, boosting, AdaBoost, Random Forests. Gaussian Processes. Model and feature selection. Learning theory: bias and variance, VC-dimension. Transformer architecture, pre-training and generative models. | |||
Contents of exercises | Implementation of regressors, classifiers and generative models using simulated and real-world data using Python. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
3 | 1 | |||
Methods of teaching | Lectures, recitals, homework and project. | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | Test paper | 60 | ||
Practical lessons | 30 | Oral examination | ||
Projects | 10 | |||
Colloquia | ||||
Seminars |