Graduate

Courses

Informatics

I529 Machine Learning in Bioinformatics

Credits: 3

Prerequisite(s): INFO-I 519 or equivalent knowledge.

The course covers advanced topics in Bioinformatics with a focus on machine learning. The course will review existing techniques such as hidden Markov models, artificial neural networks, decision trees, stochastic grammars, and kernel methods. Examine application of these techniques to current bioinformatics problems including: genome annotation and comparison, gene finding, RNA secondary structure prediction, protein structure prediction, gene expression analysis, proteonmics, and integrative functional genomics.

  • Course History

      Spring 2017


      Instructor: Yuzhen Ye
      Time: Multiple Times
      Location: Multiple Locations
      Course URL (syllabus link or course homepage)

      Spring 2016


      Instructor: Haixu Tang
      Time: Multiple Times
      Location: Multiple Locations
      Course URL (syllabus link or course homepage)
      Supplementary Description: We aim to introduce a broad range of, from fundamantal and advanced, applications of bioinformatics methods and tools to solve problems in genomics and molecular biology. In this class, we will focus on how to apply them to solving biological problems in real life.This class will have a separate lab section, in which the students will be taught in how to solve biological problems in a step-by-step fashion.

      Spring 2015


      Instructor: Cenk Sahinalp
      Time: Multiple Times
      Location: Multiple Locations
      Course URL (syllabus link or course homepage)

      Spring 2014


      Instructor: Haixu Tang
      Time: Multiple Times
      Location: Multiple Locations

      Spring 2013


      Instructor: Yuzhen Ye
      Time: Multiple Times
      Location: Multiple Locations

      Spring 2012


      Instructor: Haixu Tang
      Time: Multiple Times
      Location: Multiple Locations

      Spring 2011


      Instructor: Haixu Tang
      Instructor: Sun Kim
      Time: Multiple Times
      Location: Multiple Locations