02. Unit 0 | Review of Vectors, Planes and Optimization
05. Unit 1 | Lecture 1. Intro to Machine Learning
06. Unit 1 | Lecture 2. Linear Classifier and Perceptron
07. Unit 1. Lecture 3. Hinge loss, Margin boundaried and Regularization
08. Unit 1. Lecture 4. Linear Classification and Generalization
12. Unit 2. Lecture 5. Linear Regression
12. L5. Linear Regression.ipynb
13. Unit 2. Lecture 6. Non-Linear Classification, Kernel
14. Unit 2. Lecture 7. Recommender Systems
16. Unit 2. Project 2. Digit Recognition
17. Unit 3. Lecture 8. Feedforward Neural Networks
18. Unit 3. Lecture 9. Feedforward Neural Networks, Back Propagation and SGD
19. Unit 3. Lecture 10. Recurrent Neural Network 1
20. Unit 3. Lecture 11. Recurrent Neural Network 2
22. Unit 3. Lecture 12. CNN
23. Unit 3. Project 3. Digit recognition (Part2)
25. Unit 4. Lecture 13. Clustering 1
26. Unit 4. Lecture 14. Clustering 2
27. Unit 4. Lecture 15. Generative Models
28. Unit 4. Lecture 16. Mixture Models; EM algorithm
30. Project 4. Collaborative Filtering
31. Lecture 17. Reinforcement Learning 1
32. Lecture 18. Reinforcement Learning 2
33. Lecture 19. Applications: Natural Language Processing
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