Biophysics 203: Foundations for Mathematical Biology

• Course Director Ken Miller, 6-8217 (Additional contact info)
• Additional Lecturers:
  • Ajay Jain, 6-8218
  • Hao Li, 2-8187
  • Philip Sabes, 6-0364
  • Mark Segal, 6-4553
• T.A.'s:
  • Pinar Erciyas, 2-2930, S1080
  • Sean Collins, 2-8089, HSW1201
• Class Meeting Times:
  • Lectures: Tuesday, Thursday, 2:00-3:30, various locations (see below)
  • Discussion: Wednesday, 4:30-6:00, N729; NO Discussion Wed 9/19
  • Lecture locations:

    TUESDAY	THURSDAY	LECTURER(S)
    9/18 N517	9/20 HSW303	Miller
    9/25 N517	9/27 HSW301	Miller
    10/2 S22	10/4 N721	Miller
    10/9 N217	10/11 N217	Miller (Tu)/ Sabes (Th)
    10/16 N729	10/18 N721	Li
    10/23 N517	10/25 S18	Jain
    10/30 N225	11/1 N721	Jain (Tu) / Segal (Th)
    11/6 N517	11/8 S213	Segal (Tu) / Li (Th)
    11/13 N517	11/15 HSW303	Li
    11/20 N517		Segal
    11/27 N517		Kornak

• TOPICS:
  • 9/18 -- 10/11: Linear Algebra (Miller/Sabes): Vectors, Matrices, Coordinate systems, Linear ODEs, Eigenvectors and Eigenvalues, Complex Numbers, Fourier transforms, Diffusion Equation, Singular Value Decomposition (SVD), Normal Equations
    
  • 10/16 -- 11/22: Probability and Statistics:
    
    • 10/16-10/18 (Li): Probability distribution, moments, random-walks, Multi-dimensional Gaussian distribution and principal components analysis (PCA), Poisson process and distribution
      
    • 10/23-10/25, 10/30 (Jain): Confidence intervals, t-test, hypothesis testing, non-parametric tests, bootstrap, jacknife, permutation tests
      
    • 11/1, 11/6 (Segal): Linear and nonlinear regression, Model assessment
      
    • 11/8, 11/13-11/15 (Li): Markov chain monte carlo, Metropolis algorithm, Gibbs sampling, stochastic process, Maximum likelihood, Bayesian inference, info theory
      
    • 11/20-11/27 (Segal & Kornak): Additive models and trees (CART), Support vector machines, classification, mixture models
      
• COURSE MATERIALS:
  • Notes and problem sets for Ken Miller's lectures (pdf files):
    • Part 1
    • Part 2
    • Part 3
  • Notes for Flip Sabes' lecture (pdf file)
  • Notes for Ajay Jain's lectures (pdf files):
    • Lecture of 10/23
    • Lecture of 10/25 (Ajay's part)
    • Lecture of 10/25 (Jane's part)
    • Lecture of 10/30
  • Notes for Mark Segal's and John Kornak's lectures (pdf files, except where noted):
    • Lecture of 11/01
    • PROBLEM SET of 11/01 on regression
    • Lecture of 11/06
    • PROBLEM SET of 11/06 on model selection and assessment
    • Lecture of 11/20
    • Kornak lecture of 11/27(postscript file)
    • Book recommended (not required) for Mark Segal's lectures: The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Springer Series in Statistics) by T. Hastie, R. Tibshirani, J. Friedman (2001).
  • Notes for Hao Li's lectures (pdf files):
    • Lecture of 11/08
    • Lecture of 11/13, and problem set
    • Books recommended (not required) for Hao Li's lectures:
      • John Rice, Mathematical Statistics and Data Analysis, 2nd edition, Duxbury Press, 1995.
      • Richard Durbin et al., Biological sequence analysis: Probabilistic models of proteins and nucleic acids, Cambridge University Press, 1998.