Machine Learning

• Contents
  • Lec1. Introduction
  • Lec2. Learning (Supervised learning, Unsupervised learning) mov1 mov2 mov3
  • Lec3. Regression (Linear regression, Gradient descent, Normal equation) example
  • Lec4. Regression (Locally weighted regression, Probabilistic interpretation, Logistic regression, Newton's method) example
  • Lec5. Generative learning algorithm (Perceptron, Gaussian discriminant, Naive Bayes) example
  • Lec6. Support Vector Machine (Functional margin, Geometric margin, Optimal margin classifier) example
  • Lec7. Kernal (Kernel trick, Optimal margin classifier) example
  • Lec8. Train (Bias, Variance, Regularization, Train)
  • Lec9. Decision Tree (Decision tree, Ensemble(Bagging, Boosting))
  • Lec10. Deep learning (Neural network, activation function, loss function)
  • Lec11. Deep learning (Optimization, Backpropagation, Mini-batches)
  • Lec12. Reinforcement learning (Markov decision process, Bellman equation)
  • Lec13. Reinforcement learning (Q-learning, Deep Q network)