CS598: Machine Learning in Computational Biology Instructor: 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: Yunan Luo (yunan@illinois.edu) Location: Zoom ID TBD Time: 11:00AM - 12:15PM, Monday/Wednesday Office hours: 03:15PM - 04:45PM, Monday

Course Information

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.

Introductory materials

Machine Learning: Pattern Recognition and Machine Learning by Christopher Bishop, Cambridge University Press, 2007
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/

Course project

Schedule

Date	Presenter	Lecture/Paper
08/24/2020	Jian Peng	Introduction to this course [slides]