Welcome to Math 6040/7260: Linear Models. First of all and most importantly, I wish everyone stays safe and healthy during these challenging global times. 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-7260-2021.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 Tilton Memorial Hall 301 and Zoom https://tulane.zoom.us/j/96066182198 on Mondays and Wednesdays (Mon/Wed 10:55am-11:40am). Friday classes will be on Zoom only https://tulane.zoom.us/j/96066182198 that consist a mixture of lectures and practical lab sessions. Lectures will include a mixture of presentations using slides, plenty of chalk-board work and hands-on practicals.

Grades

There will be one mid-term and one final exam. There will be a set of homework problems (roughly) every two weeks.

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 30% 30%
Mid-term exam 30% 30%
Final exam 40% 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, Carl Friedrich Gauss, Andrey Markov, Karl Pearson, Francis Galton, John Craig
  • 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, Fingerprints & DNA fingerprints