Machine Learning 1100-3BN17
Program:
1. Introduction: linear regression and least squares method
2. Classification and logistic regression
3. Generative algorithms
4. Support vector machines
5. Introduction to neural networks, linear neural networks
6. Rosenblatt's Perceptron
7. Differentiable non-linearities and back error propagation
8. Deep neural networks
9. Unattended learning
10. Learning with reinforcement
Issues discussed theoretically during the lecture will be illustrated in practice with practical examples in the python language.
Main fields of studies for MISMaP
mathematics
physics
Mode
Prerequisites (description)
Course coordinators
Learning outcomes
Knowledge:
1. The student knows the basic concepts related to machine learning and artificial neural networks (KW01);
2. has knowledge in the field of higher mathematics and information technology necessary to solve physical problems of medium complexity using machine learning methods (KW02).
Skills:
1. The student is able to apply a machine learning approach or an artificial neural network to a practical problem (KU01);
2. can perform simple experiments, observations, numerical calculations and computer simulations using standard software packages and critically analyze the results of measurements, observations, and calculations along with the assessment of the accuracy of results (KU03).
attitudes:
1. The student appreciates the importance of machine learning methods in modern methods of data analysis (K_K06);
2. The student appreciates his own work in deepening knowledge and skills in machine learning (K_K01);
3. The student is able to properly define the priorities for the implementation of specific tasks and projects of a diverse nature (K_K03).
Expected student workload:
Participation in classes: 60 hours
Preparation for classes and solving homework assignments: 20 h
Preparation for the 10-hour exam
Preparation of the final project 20h
Assessment criteria
The grade is the average of the test result on theoretical issues and the practical tests.
Attendance at the lecture is not obligatory.
Two absences from the exercises are allowed.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: