Teaching

Course for Graduate Students:

  • Neural Computation (Spring 2008-2012, 2014-2016)

This class will cover majority of machine learning techniques. Topics Can be divided into three parts:

  1. The classical topics in neural networks, e.g., Perceptron, Multilayer Perceptron(MLP), Regularized Networks, and Self-Organization Map (SOM), and etc.
  2. A series of statistical learning theories, e.g., the classical error analysis via bias-variance decomposition, statistical learning theory (Vapanik's) and VC-dimension, regularization theory, and etc.
  3. The related learning machines (algorithms), as applications of the aforementioned learning theory, e.g. bagging, boosting (AdaBoost), Mixture of Experts, Decision Tree, Support Vector Machine, Kernel Methods and Kernel Machine, Regularization networks, and other active topics in recent machine learning community, e.g., Manifold Learning, Subspace Clustering, Compressed Sensing, Sparse Representation, Dictionary Learning, Low-Rank Representation, Matrix Completion and Sensing.

Courses for Undergraduate Students:

  • Discrete Mathematics (Spring 2008): with Xiaojie Wang
  • Digital Signal Processing (Fall 2008)
  • Basics in Bioinformatics (Spring 2012, 2014-2015)

In this course we will introduce how to use computer to handle bioinformatics data. The contents cover:

  1. Brief history of bioinformatics
  2. Basics in model biology
  3. Frequently used bioinformatics database
  4. Sequence analysis: theory, algorithms, and applications
  5. Protein structures and functions prediction
  6. Gene expression data analysis and etc.

Co-supervised Graduate Students:

Supervised Undergraduate Students:

  • 2008: Xianbiao Qi, Hui Li, LeKai Liu, Shoucheng Tang
  • 2009: Liang Chen, Houxiang Lu
  • 2010: Lin Cheng, Zhicheng Yang, Ziteng Cui, Aang Li, Han Ge, Shujing Ma, Rao Xu, Qiangke Gan