Machine Learning

The course covers a range of machine learning concepts, beginning with linear regression, naive Bayes, ensemble methods, neural networks, and reinforcement learning. In addition, the course offers an introduction to the TensorFlow library using Python and an overview of the Machine Learning Contest. It also includes hands-on projects that allow students to apply machine learning in practice.

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Entry requirement:

• The course is taught in small group (max 6).

• The minimal age to enroll is year 6.

• This course has no specific prerequisites; however, it necessitates successful completion of our KS2 computer science entry test.

• Submit the coursework before the deadline.

• The course duration is a minimum of 50 weeks.

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Goals:

Upon completing the course, students can develop a strong comprehension of the basic concepts, techniques, and algorithms applied in this domain. This knowledge can assist them in identifying typical machine learning issues, selecting suitable models, and assessing their effectiveness. The course also offers practical experience through interactive projects and assignments, enabling students to acquire problem-solving, critical thinking, and programming skills. Furthermore, it serves as a foundation for students interested in participating in machine learning contests.