Statistical Machine Learning Methods for
Bioinformatics
Instructor: Jianlin Cheng
Course Web: http://www.cs.missouri.edu/~chengji/mlbioinfo/mlbioinfo.htm
Department:
Term: Spring Semester, 2010
Objectives:
This course teaches statistical machine learning methods and their applications in Bioinformatics. The course intends to achieve two major goals. The first goal is to help students understand the theories of advanced machine learning methods. The second goal is to teach students how to develop Bioinformatics tools using the methods.
Topics: 1. Hidden Markov models and their applications in
bioinformatics 2. Neural networks and their applications in
bioinformatics 3. Support vector machines and their applications in
bioinformatics 4.
Bayesian networks and their applications in bioinformatics Homework: A
comprehensive project of applying machine learning methods in Bioinformatics Grading:
Project
presentation (50%) and report (50%)
Disability Accommodations
If you need accommodations because of a disability, if you have emergency medical information to share with me, or if you need special arrangements in case the building must be evacuated, please inform me immediately. Please see me privately after class, or at my office.
Office location:_______________ Office hours : __________
To request academic accommodations (for example, a note taker or extended time on exams), students must also register with the Office of Disability Services (http://disabilityservices.missouri.edu), S5 Memorial Union, 882-4696. It is the campus office responsible for reviewing documentation provided by students requesting academic accommodations, and for accommodations planning in cooperation with students and instructors, as needed and consistent with course requirements. For other MU resources for students with disabilities, click on "Disability Resources" on the MU homepage.
Academic Integrity
Academic integrity is fundamental to the activities and principles of a university. All members of the academic community must be confident that each person's work has been responsibly and honorably acquired, developed, and presented. Any effort to gain an advantage not given to all students is dishonest whether or not the effort is successful. The academic community regards breaches of the academic integrity rules as extremely serious matters. Sanctions for such a breach may include academic sanctions from the instructor, including failing the course for any violation, to disciplinary sanctions ranging from probation to expulsion. When in doubt about plagiarism, paraphrasing, quoting, collaboration, or any other form of cheating, consult the course instructor.