[09/20]

[09/20] The introduction of this course [pptx]
[09/20] Intro to Machine Learning [pdf]
[09/20] Regression [pdf1] [pdf2]
[09/20] The tutorial is online! [GitHub] [Pytorch]

[09/27]

[09/27] Probabilistic Generative Model, Logistic Regression [pdf1] [pdf2]
[09/27] HW1: prediction of PM2.5 [ppt] [report template] [Kaggle]
[09/27] HW1: hand writing [HackMD]

[10/04]

[10/04] Introduction to DNN [pdf1] [pdf2]
[10/04] Back Propgation, Gradient Descent [pdf1] [pdf2]
[10/04] Logistic Regression [pdf]
[10/04] HW1 sample from TA [pdf]
[10/04] bash tutorial [pdf]

[10/05]

[10/05] Introduction to CNN [pdf1] [pdf2]
[10/5] HW2: Income prediction [ppt] [report template] [Kaggle]

[10/18]

[10/18]PCA [pdf]

[10/25]

[10/25]autoencoder [pdf]
[10/25]TSNE [pdf]
[10/25] Pytorch toturial [pdf]
[10/25] HW3: Image Sentiment Classification [ppt] [report template] [Kaggle]
[10/25] HW3: hand writing [HackMD]

[11/01]

[11/01]ensemble [pdf]

[11/08]

[11/08] RNN [pdf]
[11/08] HW4: Image Clustering [ppt] [report template] [Kaggle]
[11/08] HW4: hand writing [PDF]

[11/22]

[11/22] Expectation Maximization [pdf]
[11/22] HW5: [ppt] [report template] [Kaggle]
[11/22] HW4: hand writing [HackMD]

[11/29]

[11/29] Semi-supervised Learning [pdf]

[12/06]

[12/06] Variational Autoencoder [pdf]
[12/06] Support Vector Machine [pdf1] [pdf2]

[12/13]

[12/13] Support Vector Machine [pdf1] [pdf2] [pdf3]

[12/20]

[12/20] Support Vector Machine and Duality [pdf]

[12/27]

[12/27] PAC Learning [pdf]

[01/03]

[01/03] Final Exam Solution [pdf]