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.
- Fri, Sep 21: Linear Algebra, continued.
- Mon, Sep 24: Eigenvalues and Eigenvectors, Symmetric Matrices
- Wed, Sep 26: Review for exam on Friday. No new homework.
- Fri, Sep 28: Exam 1 (For the take home exam, see links below)
- Mon, Oct 1: The Singular Value Decomposition. No new HW- Work on the take home exam.
- Wed, Oct 3: Applications of the SVD: No new HW- Finish the take home exam.
- (Oct Break)
- Mon, Oct 8: Finish the SVD and the Pseudoinverse. Computer Lab: Clown image and the SVD
- Wed, Oct 10: Discuss the homework (Due: Friday, Oct 12, 11:59PM). The homework is #5, 6.. Started the
notes. We'll finish these on Friday.
- Fri, Oct 12: Finish the "Best Basis".
- Mon, Oct 15: Discussed applications of the best basis to photos, started linear neural nets.
- Wed-Fri, Oct 17-19: Linear neural networks, The Hebb learning rule.
- Mon-Fri, Oct 22-26: Data Clustering
- Monday-Wednesday, Oct 29-31
- Fri, Nov 2: Optimization
- Monday-Friday, Nov 12-16: Radial Basis Functions
- Lab materials
- Monday-Friday, Nov 26-30: Feed forward Neural Networks
- Last Week (Mon, Wed) Deep Networks
Exam Review Links
Take Home Exam Links
- Take Home Exam 2 (Due Wednesday evening, Nov 14)
- Final Exam Links (Due Thursday, Dec 13th, 8PM)