## CS598: Machine Learning in Computational BiologyInstructor: Jian Peng Assistant Professor Department of Computer Science University of Illinois at Urbana-Champaign 2118 Siebel Center 201 N Goodwin Ave Urbana, IL, 61801 Email: jianpeng AT illinois.edu Teaching Assistant: Rongda Zhu (rzhu4@illinois.edu) Location: 4101 Materials Science & Eng Bld Time: 02:00PM - 03:15PM, Tuesday/Thursday Office hours: 03:15PM - 04:45PM, Thursday |

This course focuses on modern machine learning techniques in computational biology, including probabilistic modeling, feature selection, graphical models, approximate inference and learning, Monte Carlo methods and neural networks. Students will learn the development of the theoretical concepts for these methods and the applications of these methods to a variety of problems in computational biology. This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms is expected.

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners.

Biology: Molecular Biology for Computer Scientists by Larry Hunter. Downloadable PDF here.

You can learn more from online videolectures at http://ocw.mit.edu/courses/biology/7-012-introduction-to-biology-fall-2004/video-lectures/

Date | Presenter | Lecture/Paper |

08/25/2015 | Jian Peng | Introduction to this course [slides] |

08/27/2015 | Jian Peng | Sequence data: part 1 [slides] Papers for variational inference: Neural Variational Inference and Learning in Belief Networks Stochastic Variational Inference |

09/01/2015 | Jian Peng | Sequence data: part 2 [slides] Logistic regression and maximum entropy models. Introduction to Conditional Random Fields |

09/03/2015 | Jian Peng | Matrix data: part 1 [slides] A Neural algorithm of artistic style Sequence-to-sequence learning with neural networks Deep learning for protein structure prediction Conditional Neural Fields |

09/08/2015 | Jian Peng | Matrix data: part 2 [slides] Sparse regression Subgradient SVD in Netflix challenge Restricted Boltzmann Machines for Collaborative Filtering |

09/10/2015 | Jian Peng | Network data [slides] |