Introduction to ML#
The key word is introduction. Completing this course one should be acquire knowledge and skills which serve as prerequisits for studying specific branches of machine learning and their applications in depth.
Machine learning = Math + Programming
Machine learning is a dynamic and interdisciplinary field that has emerged from the symbiotic combination of mathematics and programming. At its core, machine learning leverages mathematical principles, particularly statistics, linear algebra, and calculus, to develop algorithms that enable computers to learn from data and make predictions or decisions without explicit programming. The fusion of these mathematical foundations with programming languages such as Python and R has led to the creation of powerful frameworks and libraries, facilitating the implementation and deployment of complex machine learning models.
Sincerely yours, chatGPT
Hence, the course constists of the following parts:
mathematics for ML
Python libraries and frameworks for ML
basic models of ML
Course assessment#
Activity |
Final scores |
|---|---|
Attendance and participation |
\(10\%\) |
Practice (SIS) |
\(20\%\) |
Mid-term |
\(15\%\) |
End-term |
\(15\%\) |
Final exam |
\(40\%\) |
Practice consists of assignments in Jupyter Notebooks.
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