Jun Zhu's Homepage

Jun Zhu

Prof. @ THU

Statistical Machine Learning, Fall 2015

Slides

Lecture 1: Introduction & Logistics.
Lecture 2: Clustering.
Lecture 3: Dimension reduction.
Lecture 4: Support Vector Machines.
Lecture 5: Naive Bayes and Logistic Regression.
Lecture 6: Learning Theory.
Lecture 7: Probabilistic Graphical Models (I): Representation.
Lecture 8: Probabilistic Graphical Models (II): Inference & Learning.
Structured Learning.
Lecture 9: Bayesian Methods (I).
Monte-Carlo Methods.
Lecture 10: Nonparametric Bayesian Methods (Dirichlet Process Mixtures).
Lecture 11: Latent Feature Learning.
Lecture 12: Nonparametric Bayesian Methods (Gaussian Processes).
Lecture 13: Sparse Learning.
Lecture 14: Deep Learning.

Assignments

Homework 1: EM and PCA.
Homework 2: SVM, exponential family and logistic regression.
Homework 3: Graphical models, learning theory and bayesian methods.
Homework 4: Sparse learning and nonparameteric Bayes.

Acknowledgements

We acknowledge the contributions from many friends, including Eric P. Xing, Aarti Singh, and etc. The slides were built based on many great sources. Feel free to let us know if your materials were included or if you like to include your materials in future versions. We thank you all in advance.

Last updated on Dec. 10th, 2019. Count: .