@Purdue: CS 578 Statistical Machine Learning by Yexiang Xue (My Notes); CS 577 Natural Language Processing
@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++
ML Packages:   Pytorch · scikit-learn · TensorFlow