Welcome to Math 6040/7260: Linear Models. Here is some essential information to get started with Math 6040/7260.
Class webpage
Please bookmark and visit the course webpage frequently for the most updated information: https://tulane-math-7260-2022.github.io/.
Lecture format
With the COVID-19 pandemic going on, the lectures will be delivered in a hybrid mode. Exams will be take-home to accommodate any future uncertainties.
I will deliver lectures at Howard-Tilton Memorial Hall 116 and Zoom https://tulane.zoom.us/j/94748340296 on Mondays and Wednesdays (Mon/Wed 10:55am-11:40am). Friday classes will be on Zoom only https://tulane.zoom.us/j/96583956151 that consist a mixture of lectures (first 2/3 weeks) and practical lab sessions. Lectures will include presentation slides and questions.
Attendance
I do require attendance. However, you could attend classes through any of the following format
- In person (Monday / Wednesday)
- Zoom (Monday / Wednesday / Friday)
- Watching zoom recordings
Grades
There will be one mid-term and one final exam. There will be roughly 4 sets of homework problems.
All graduate students are required to deliver a 15-min talk (10 min presentation + 5 min questions) on one of the following topics (secure yours before others do). You may suggest a topic outside of the pool too. Undergraduate students are encouraged to participate too with bonus 5 points towards the final score.
| Math-6040 | Math-7260 | |
|---|---|---|
| Homework | 40% | 30% |
| Mid-term exam | 30% | 30% |
| Final exam | 30% | 30% |
| Presentation | 5% (bonus) | 10% |
Topic pools
The topics are given by key words only. Please practice your ability of “educated” searches with google.
- Software key words: stan, bugs & jags, hadoop, spark, tensorflow, Scikit-Learn, Blas & Lapack
- Stastician key words: R. A. Fisher, Andrey Markov, Karl Pearson, Francis Galton, John Craig, George Box, David Cox, William Cochran, Gertrude Cox,
- Research areas: Causal Inference, Forensic Statistics, Bayesian Statistics, Approximate Bayesian Computation, Sequential Monte Carlo method, Variational Bayes, Spatial statistics, Precision Medicine
- Parallel computing: OpenCL, Cuda, SIMD (SSE + AVX)
- Other: Frequentist vs. Bayesian debate, Algorithms behind Machine Learning