Evaluation is based on the following methods:
- 30% Weekly reports
- 30% Class participation
- 40% Exams
By the end of the course, the student must be able to:
- Define Regression and Classification, and explain the main differences between them
- Describe a few models and algorithms for them.
- Implement and apply these methods.
- Derive the theory behind ML methods taught in the course and generalize them to new problems.
- Continue to work through difficulties or initial failure to find optimal solutions.
- Assess one’s own level of skill acquisition, and plan their on-going learning goals.