Bayesian Statistics
Techniques and Models

https://www.coursera.org/learn/mcmc-bayesian-statistics/home/welcome

How to get better at math as a fresh college graduate?

The title is pretty self-explanatory. I recently graduated from a CS bachelor degree which included a few math courses (ie. algebra, calculus, differential equations, analytic geometry, probabilities and statistics and numerical computation). I always liked math ever since high school, however in uni it wasn't the focus of my degree.

Fast forward to now, I'm really interested in machine learning and it applications and want to get a better grasp on its theoretical concepts. For this reason I started reading An Introduction to Statistical Learning. I also started doing two courses on Coursera on Bayesian statistics (this one and it sequel) to strengthen my understanding of probabilities.

Now comes the problem. Although I find that the courses do help me and I can follow the proofs in the book with some effort, I don't really feel that I practice the concepts enough. Now that there are no live classes with homework and assignments I don't really know how to practice math by myself. Coursera does offer that to some extent, but it's a limited and small set of exercises.

So the question is, /r/learnmath: how do you practice math at a college level all by yourself? How to you chose what exercises to do and what resources do you use? How do you get to a level where you could write a simple proof like some of those in the book all by yourself?

• Part of "Statistics with R" specialization. I can recommend it to someone who has fair knowledge of confidence intervals, hypthesis testing etc. since it does a great job in comparing classical statistical methods with their Bayesian counterparts: https://www.coursera.org/learn/bayesian/home/welcome

• Nice class that teaches you basic stuff about how MCMC works and lets you play with it in JAGS: https://www.coursera.org/learn/mcmc-bayesian-statistics/home/welcome

• This is big, quite complex specialization that teaches about graphical models that have knowledge engineering, priors and Bayesian inference as their primary ways of building and training the models. It does go over MCMC. I would not recommend this specialization to someone who wants to start learning, but someone who is fairly familiar with MCMC and variational inference would find a lot of cool stuff in PGMs that were "the best thing" before deep learning revolution: https://www.coursera.org/specializations/probabilistic-graphical-models

• Bayesian Methods for Hackers is an easy to read book (available online as a github repo with all source code) that shows some of the tricks that are extremely difficult to pull off if you are using more commonplace MLE methods. This is highly recommended: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

There are two. I jumped right into the second one, but they seem to reference the first course quite a bit:

1st level: https://www.coursera.org/learn/bayesian-statistics

2nd level: https://www.coursera.org/learn/mcmc-bayesian-statistics

Started a course in bayesian statistics. It's going very smoothly, the instructor is very clear and the problems are interesting. It might be too basic though, since I keep getting 100% in the problem sets (even in the honors). This is the first week, perhaps by the second week things will be a bit harder. There's a second course as a followup which looks even more interesting. Can't wait to start that too.