Course 4190.515: Bioinformatics
(Machine Learning)


School of Computer Science and Engineering, Seoul National University

Instructor
Prof. Byoung-Tak Zhang
TA:
Jeong-Ho Chang, jhchang@scai.snu.ac.kr, Tel.: 880-1847, Room 301-419
Classroom
301-101
Time
    Monday and Wednesday, 16:00-17:15
Textbook

    Bioinformatics: The Machine Learning Approach, P. Baldi and S. Brunak, A Bradford Book, The MIT Press, 1998

Requirements

    Two exams: 40 %, two projects: 40%, homeworks: 15%, class participation: 5%

    Project 1: Gene finding with neural networks
    Project 2: Protein modeling with hidden Markov models
Objectives
  • Understanding the current issues in bioinformatics
  • Understanding the principles of important machine learning algorithms
  • Understanding how to apply machine learning to biological data mining


  • Important Schedule
May 21, 2001 Deadline for the draft term paper (NN only)
June  4, 2001 Final Exam
June 18, 2001 Project presentations
June 25, 2001 Deadline for the final term paper (NN + HMM)
  • Schedule
Week Topics
Week 1

Introduction to Molecular Biology

Week 2

Genomics, Proteomics, and Bioinformatics

Week 3

Neural Networks: Theory(1)  Theory(2)

Week 4

Neural Networks: Applications
Assignment of Project 1

Week 5

Probabilistic Framework, Bayesian Inference, Information Theory, Occam's Razor
Probabilistic Modeling and Inference

Week 6 Introduction to Graphical Models and Bayesian Networks
(Nilsson - chapter 20, Mitchell - chapter 6)
- Reference -
  • Artificial Intelligence: A New Synthesis, Nils J. Nilsson, Morgan Kaufmann, 1998.

  • Machine Learning, Tom Mitchell, McGraw-Hill, 1997.
  • Week 7 Expectation-Maximization (EM) and Variants
    (Updated in 5.1)

    Report Number 1 (in place of Mid Exam)
    Week 8 Hidden Markov Models (HMMs): Theory(1)  Theory(2)
    Week 9 Hidden Markov Models: Applications
    Assignment of Project 2
    Week 10 Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, Simulated Annealing
    Week 11 Evolutionary Computation, Importance Sampling, Active Learning, Constructive Learning
    Week 12 Latent Variable Models
    Week 13 Helmholts Machines
    Week 14 Final Exam
    Week 15 Project Presentations
    Week 16 Project Presentations


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