1. Feature vector
  2. Classifiers
  3. Classification vs Regression
  4. Linear Classifiers
  5. Gradient Descent
  6. Linear Regression
  7. Nonlinear Classification
  8. Recommender systems – K-Nearest Neighbor
  9. Introduction to Deep Neural Networks
  10. Back-propagation Algorithm
  11. Recurrent Neural Networks (RNNs)
  12. Convolutional Neural Networks (CNN)
  13. Unsupervised learning
  14. Generative vs Discriminative models
  15. Mixture Models and the Expectation Maximization (EM) Algorithm
  16. Learning to Control: Introduction to Reinforcement Learning
  17. Revisiting MDP Fundamentals

Machine Learning lecture notes – Source : MIT OpenCourseWare: https://ocw.mit.edu/index.htm


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