Math 350, Fall 2018
This course explores the process of building, analyzing, and
interpreting mathematical descriptions of physical processes.
Topics may include feature extraction, partial differential
equations, neural networks, statistical models. The course
will involve some computer programming, so previous programming
experience is helpful, but not required.
There is no required text for this course. Class notes will be provided.
- Day 1-2:
- Mon, Sep 3: Finish covariance.
- Homework: Exercises 5-11 on pg. 20 of the "First Day notes" (previous day's link). Due on Friday (Exercise 6 will be done in class).
- Wed, Sep 5: Linear regression (in the notes, right after the stats).
- Homework: For the Hanford data at the end of Day 1 notes (p. 24 of the first handout above), find the line of best
fit using the normal equations and Matlab. Also provide a plot of the data points and the line you found. DUE: Monday, Sep 10.
- Fri, Sep 7: n-armed bandit (paper handed out last time)
- Mon, Sep 10: Variance and projections, n-armed bandit.
- Variance and Projection notes and HW Work on the exercises for Friday.
- Matlab HW: Run the n-armed bandit example and publish the script (with the ending plot). Due: Thur, Sep 13, 10PM (This shouldn't
take too long- Copy and paste the Matlab code from the PDF handout).
- Wed, Sep 12: Finish n-armed bandit, discuss functions in Matlab (vs scripts).
- Homework: Exercises 1, 2, 5 on pg 12 of the n-armed bandit notes (linked today). Due on Monday, Sep 17.
- Matlab handout: Functions versus scripts.. Work on the homework problems included therein
for Wednesday, Sep 19.
- Fri, Sep 14: Genetic algorithms.
- Mon, Sep 17: Finish Genetic Algorithms.
- Finish the genetic algorithm notes. HW for Monday, Sep 24 (Updated deadline). Solve the Knapsack problem in Matlab.
- Wed, Sep 19: Linear Algebra notes.