Machine_Learning

Machine Learning

Lecture notes

• Learning theory - When can machines learn
• Learning theory - Why can machines learn
• Supervised learning
• Unsupervised learning
• Reinforcement learning

Lab

• Lab 1 - Linear regression and PLA
• Lab 2 - Logistic regression
• Lab 3 - Nonlinear transformation
• Lab 4 - Noise
• Lab 5 - Regularisation
• Lab 6 - Deep learning
• Lab 7 - SVM and PCA

Midterm

May 7, 10:15-12:00, Lab 322.

Lab submissions

For each lab some of the exercises have to be submitted right after the lab (lab work), for the rest (lab assignments), you will have about a week before the submission deadline.

Lab work submissions will only have a pass/fail score.

Course requirements

Grading is based on three components:

• Lab assignments (25%)
• Midterm (25%)
• Final (50%)

To complete the semester and receive a signature you have to meet two conditions:

1. submit each lab assignment and either achieve at least 25% on each of them or an overall 50% on their sum,
2. pass at least 60% of lab work submissions (to be submitted right after class and scored as pass/fail)
3. achieve at least 50% on the midterm exam, and at least 40% on both parts of it.