Subjects in machine learning I'm in the second year of my mechanical engineering bachelor and I've recently started exploring the field of machine learning online. My question is this: which specific subjects do people learn, and in what order, when they study machine learning in college? Also, which books would you recommend? I'm NOT asking which online courses to take, I just want to know which SUBJECTS you were taught in college. For example, I've been doing a lot of reading lately, and it was only yesterday that I came across discriminative/generative networks, yet it seems important to me. 
I'm nearly done with prof. Andrew Ng's course on Coursera, but I feel like I'm missing out on a lot of details and concepts. I'd like to hear which subjects you studied (and perhaps in what order). 
 A: Andrew Ng's Coursera course is really basic one. An other similar level but more complete resource is Introduction to statistical learning, and more advanced one Elements of Statistical Learning.
If you really want to go deeper here is my suggestions on the topic


*

*Linear algebra / Matrix algebra / Multivariate calculus

*Probability and statistics

*Numerical Analysis

*Data structure and Algorithm

*Continuous Optimization (super important), must to read book: Convex Optimization
A: Start reading a bit of everything, investigate problems and solutions people propose, see what interests you more. There are some papers comparing different algorithms in some tasks, I find these the most entertaining to read.
I started working with LDA and KNN in college, for example. When I couldn't really achieve my goals with it I moved onto trees and then to kernel methods, which really interest me to this day. All the while I studied a lot on resampling and optimization strategies, evaluation metrics and heuristics, as this has a lot to do with kernel selection in SVMs and other associated methods.
I never touched some really popular topics, like neural networks. If I were to start again, I'd like to have a good understanding of PCA and ICA and, above all, proper cross-validation procedure.
