Fundamentals of programming mathematical algorithms for ML
Description: The course combines the basics of Python with linear algebra for machine learning. Students will master NumPy, operations with vectors and matrices, as well as key algorithms of the principal component method and singular matrix decomposition. Practical tasks include the implementation of linear regression methods and working with data. The course will prepare you for an in-depth study of ML libraries.
Amount of credits: 6
Пререквизиты:
- Информатика. Школьный курс
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 30 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 90 |
| Form of final control | Exam |
| Final assessment method |
Component: University component
Cycle: Base disciplines
