• Powerpoint version of the slides: link
  • Introduction of this course pdf (2016 /09/23)
  • HW0 pdf, video (2016 /09/23)
  • Learning Map pdf, video (2016 /09/30)
  • Regression: Case Study pdf, video (2016 /09/30)
  • Gradient Descent pdf, video (2016 /10/07)
  • HW1 pdf (2016/09/30)
  • Where does the error come from? pdf, video (2016 /10/07)
  • Classification: Probabilistic Generative Model pdf, video (2016 /10/07)
  • Classification: Logistic Regression pdf,video (2016/10/14)
  • HW2 pdf (2016/10/15)
  • Brief Introduction of Deep Learning pdf,video (2016/10/14)
  • Backpropagation pdf,video (2016/10/28)
  • “Hello world” of deep learning pdf,video(2016/10/28)
  • Tips for deep learning pdf,video (2016/11/04)
  • Convolutional Neural Network pdf,video (2016/10/28)
  • HW3 pdf (2016/10/28)
  • Why deep pdf,video (2016/11/04)
  • Semi-supervised Learning pdf,video (2016/11/11)
  • Unsupervised Learning: Linear Dimension Reduction pdf,video (2016/11/11)
  • HW4 pdf (2016/11/18)
  • Unsupervised Learning: Word Embedding pdf,video (2016/11/25)
  • Unsupervised Learning: Neighbor Embedding pdf,video (2016/12/02)
  • Unsupervised Learning: Deep Auto-encoder pdf,video (2016/11/18)
  • Unsupervised Learning: Deep Generative Model pdf,video (part 1),video (part 2) (2016/12/02)
  • Final rule,project1,project2,project3 (2016/12/02)
  • Transfer Learning pdf,video (2016/12/09)
  • Support Vector Machine (SVM) pdf,video (2016/12/09)
  • Structured Learning: Introduction pdf,video (2016/12/09)
  • Structured Learning: Linear Model pdf,video (2016/12/09)
  • Structured Learning: Structured SVM pdf,video (2016/12/12)
  • Structured Learning: Sequence Labeling pdf,video (2016/12/23)
  • Structured Learning: Recurrent Neural Network pdf,video (part 1),video (part 2) (2016/12/30)
  • Ensemble pdf,video (2016/01/06)
  • Deep Reinforcement Learning: Scratching the surface pdf,video (2016/01/06)