Machine Learning Roadmap

I am going to study machine learning, and wanna take notes here. Since I majored in English during my college days, I don't know much about mathematics. My plan is to go through the following subjects very quickly to get a rough understanding of mathematics before getting started with machine learning itself.

A, Calculus

I'll focus on limit theory, derivatives and Taylor series

B, Linear algebra

I'll focus on subspace, linear transformation, eigenvalue and eigenvector, matrix decomposition, SVD and Markov china

C, Probability and statisctics

I'll get familiar with classic probability models, random variables, probability distribution, mean, variance and covariance

D, Convex optimization

Convex optimization techniques are frequently used in machine learning to reduce loss functions and adjust model parameters, I've to familiarize myself with the basic ideas and common techniques of convex optimization

E, Random process

I don't know much about this area, but seems that the Hidden Markov Model and Conditional Random Field and other time series models have something to do with this. I noted that in natural language processing, they use these two models to segment sentences into words for later processing.

I think that's enough for me to have solid mathematical knowledge. Roughly I'll spend one more month to getting familiar with all these.After that, I should read books covering the following topics to dive deep into machine learning

A, Machine Learning theories and algorithms

B, Statistical elements of machine learning

C, Deep learning

D, Reinforcement learning

I don't know much about these currently, maybe I'll get started with them soon.