Class Outline

Here’s the tentative outline for MML:

  • Week 1:
    • Mon, Jan 13: Intro - Overview of the course and a couple groovy examples
    • Wed, Jan 15: Calculus - Functions, derivatives, and integrals (as we need them)
    • Fri, Jan 17: Lab 1 - Python intro via calculus
  • Week 2:
    • Wed, Jan 22: Multivariable calculus - Application to linear regression
    • Fri, Jan 24: Sects 2.1-2.3 - Systems, Matrices, Solving systems
  • Week 3:
    • Mon, Jan 27: Sects 2.4,2.5 - Vector spaces, Linear Independence
    • Wed, Jan 29: Review
    • Fri, Jan 31: Exam 1
  • Week 4:
    • Mon, Feb 3: Sect 2.6 - Basis/Rank
    • Wed, Feb 5: Sect 2.7 - Linear Mappings
    • Fri, Feb 7: Lab 2 - Linear algebra with Python
  • Week 5:
    • Mon, Feb 10: Sects 3.1-3.3 - Norms, Inner products, Length
    • Wed, Feb 12: Sects 3.4-3.6 - Orthogonality
    • Fri, Feb 14: Lab 3 - Regression line via projection
  • Week 6:
    • Mon, Feb 17: Clustering - A quick look at K-nearest neighbors and K-means.
    • Wed, Feb 19: Review
    • Fri, Feb 21: Exam 2
  • Week 7:
    • Mon, Feb 24: Logistic Regression - Logistic regression in theory and in practice
    • Wed, Feb 26: Lab 4 - Regression in the real world
    • Fri, Feb 28: Sect 4.1 - Trace, determinant
  • Week 8:
    • Mon, Mar 3: Sect 4.2 - Eigenvalues and eigenvectors
    • Wed, Mar 5: Eigenranking - Using Google PageRank to rank sports teams
    • Fri, Mar 7: Lab 5 - Eigenranking
  • Week 9:
    • Mon, Mar 17: Sect 4.3 - Eigen-decomposition
    • Wed, Mar 19: Review
    • Fri, Mar 21: Exam 3
  • Week 10:
    • Mon, Mar 24: Sect 5.2 - Partial derivatives and gradients
    • Wed, Mar 26: Sects 5.7,5.8 - Higher derivatives, linear and quadratic approximation
    • Fri, Mar 28: Neural Networks - An overview of neural networks
  • Week 11:
    • Mon, Mar 31: Sects 5.3,5.4 - Jacobians
    • Wed, Apr 2: Sect 5.6 - Backprogation
    • Fri, Apr 4: Lab 6 - Neural Networks
  • Week 12:
    • Mon, Apr 7: Sect 7.2 - Constrained optimization with Lagrange multipliers
    • Wed, Apr 9: Review
    • Fri, Apr 11: Exam 4
  • Week 13:
    • Mon, Apr 14: Sect 6.2 - Discrete and continuous random variables
    • Wed, Apr 16: Sect 6.5 - The normal distribution
    • Fri, Apr 18: Lab 7 - Distributions with Python
  • Week 14:
    • Mon, Apr 21: Sect 6.3 - Bayes’ theorem
    • Wed, Apr 23: Review
    • Fri, Apr 25: Exam 5
  • Last day:
    • Mon, Apr 28: Wrap up
  • Final:
    • Mon, May 5: Final lab - I expect to have a final lab incorporating mulitple ideas from the semester.