@Purdue: CS 578 Statistical Machine Learning; CS 577 Natural Language Processing; CS 573 Data Mining; CS 592 Machien Learning Theory; CS 580 Algorithms; CS 503 Operating Systems; STAT 519 Probability; STAT 528 Mathematical Statistics; STAT 546 Computational Statistics; STAT 524 Multivariate Analysis; STAT 540 Quantitative Finance
@XJTU: Foundations of Optimization by Minnan Luo; Machine Learning; Computer Vision and Pattern Recognition by Yuanqi Su; Natural Language Understanding and Machine Translation; Complex Network Dynamics; Numerical Analysis; Mathematical Logic; Combinatorial Mathematics.
@Berkeley: CS 188 Introduction to Artificial Intelligence by Pieter Abbeel; CS 186 Introduction to Database Systems by Joseph M. Hellerstein; CS 61c Great Ideas of Computer Architecture (Machine Structures) by Dan Garcia.
Online Courses I Recommend:
@Stanford: CS 231n Convolutional Neural Networks for Visual Recognition by Fei-Fei Li
@Berkeley: CS 294 Deep Reinforcement Learning by Sergey Levine
@CMU: 10-601/701 Machine Learning by Tom Mitchell
@Stony Brook: CSE 373 Analysis of Algorithms by Steven Skiena
@UPenn: CIS 625: Theory of Machine Learning by Michael Kearns
Skills
Basic Programing Laguages: Python · C/C++ · MATLAB · R
