课时1：（0-1）Introduction of Machine Learning

课时2：(0-2)Why we need to learn machine learning ?

课时3：(1-1)回归-案例研究

课时4：（1-2）Regression - Demo

课时5：（2）Where does the error come from ?

课时6：（3-1）Gradient Descent

课时7：（3-2）Gradient Descent (Demo by AOE)

课时8：（3-3）Gradient Descent (Demo by Minecraft)

课时9：（4）Classification

课时10：（5） Logistic Regression

课时11：（6）Brief Introduction of Deep Learning

课时12：（7）Backpropagation

课时13：（8-1）“Hello world” of deep learning

课时14：（8-2）Keras 2.0

课时15：（8-3）Keras Demo

课时16：（9-1）Tips for Training DNN

课时17：（9-2）Keras Demo 2

课时18：（9-3） Fizz Buzz in Tensorflow (sequel)

课时19：（10）Convolutional Neural Network

课时20：（11）Why Deep ?

课时21：（12）Semi-supervised

课时22：（13）Unsupervised Learning - Linear Methods

课时23：（14）Unsupervised Learning - Word Embedding

课时24：（15）Unsupervised Learning - Neighbor Embedding

课时25：（16）Unsupervised Learning - Auto-encoder

课时26：（17）Unsupervised Learning - Deep Generative Model (Part I)

课时27：（18）Unsupervised Learning - Deep Generative Model (Part II)

课时28：（19）Transfer Learning

课时29：（20）Support Vector Machine (SVM)

课时30：（21-1）Recurrent Neural Network (Part I)

课时31：（21-2）Recurrent Neural Network (Part II)

课时32：（22）Ensemble

课时33：（23-1）Deep Reinforcement Learning

课时34：（23-2）Policy Gradient (Supplementary Explanation)

课时35：（23-3）Reinforcement Learning (including Q-learning)