13M051MU - Machine Learning
Course specification | ||||
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Course title | Machine Learning | |||
Acronym | 13M051MU | |||
Study programme | Electrical Engineering and Computing | |||
Module | Signals and Systems | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | ||||
The goal | Introduce students to theoretical and practical aspects of supervised machine learning and reinforcement learning. illustration of various application areas and techniques for choosing the adequate modeling, optimization, evaluation and implementation techniques. | |||
The outcome | Students will be able to: choose an adequate machine learning algorithm suited to the real-world problem at hand, 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 | ||||
URL to the subject page | http://automatika.etf.bg.ac.rs/sr/13m051mu | |||
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. Reinforcement learning. | |||
Contents of exercises | Implementation of regressors and classifiers of real-world data using Matlab/Octave, Python or R. Reinforcement learning in simulated environments. | |||
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 | 20 | Oral examination | ||
Projects | 20 | |||
Colloquia | ||||
Seminars |