Computer Science / Math track
As I was going through the Coursera Machine Learning course and learning about Speech Recognition and reading papers I see that one gap in my knowledge is statistics. For example terms like this: Hidden Markov Models, posterior probabilities, Bayesian statistics, cross entropy, Gaussian, negentropy, doubly stochastic process, Cauchy density. I have heard of simpler things like p-values or confidence intervals, but I wouldn’t know how to produce them if I needed.
Therefore I decided that for the CS track I will focus on statistics and probability for the next quarter. (Beauty of making up my own curriculum and being able to adjust real time to the needs I have at work.)
I looked at few stats courses and I have settled on Udacity’s: Intro to Descriptive Stats (https://www.udacity.com/courses/ud827) & Intro to Inferential Stats (https://www.udacity.com/courses/ud201).
Udacity has a mobile app so I can watch videos or do quizzes while on the bus for example, which does add to the convenience. Also, Udacity recently came out with Machine Learning Engineer Nanodegree which looks very interesting, it includes a very new course on Deep Learning in collaboration with Google. (https://www.udacity.com/course/deep-learning–ud730)
To throw in some fun reading about statistics I plan to
* finish reading The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t by Nate Silver (amazon)
* read Supreforecasting: The Art and Science of Prediction by Tetlock and Gardner (amazon)
I commit to finishing Intro to Descriptive stats course for this quarter and will see how far I will get with the rest.