- Components of Machine Learning: Data, Hypothesis Space and Loss Functions
- Algorithms for Machine Learning: Gradient Descent, Greedy Search, Linear Solvers
After completing the course, the students
- can formalize applications as ML problems and solve them using basic ML methods.
- understand the concept of generalization and how to analyse it using simple probabilistic models.
- are familiar with linear models for regression and classification.
- know how basic ML methods are obtained as combinations of particular choices for data representation (features), hypothesis space (model) and loss function.
- are familiar with the idea of hard and soft clustering methods.
- understand the basic idea of feature learning methods.
5 credits, approx 130 hours of work divided into:
- lectures + self-study (30 hours)
- assignments (6 * 10 = 60 hours)
- project work (around 40 hours)
For Aalto degree students: This course overlaps with Machine Learning: Basic Principles (CS-E3210) and Machine Learning with Python (CS-EJ3211) and only one of them can be included in the degrees. If you have already taken one of the basic machine learning courses, you should take the course Machine Learning: supervised methods (CS-E4710) instead.
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