

Machine Learning Course for Industry
Here is the Machine Learning course I designed for Industry Data Scientists; Some of these topics also forms part of my research (e.g., Deep Learning).

Intro to Data Analytics and Machine Learning This is a warmup with some terminologies; Along with the notes, there are heaps of demos for research and REAL WORLD industry applications. 

Intro to Bayesian Statistics Explain common probability concepts such as marginal, conditional distribution and Bayes formula etc 

Optimization Going through the basic idea behind iterative algorithms such as Stochastic Gradient Descend 

Regression Linear, Polynomial, Mixed Effects and some classification algorithm bear the name of regression: multinomial regression 

Recommendation System collaborative Filtering, nonnegative factorization, (using multiplicative rule) 

Dimensional Reduction Dimensionality Reduction techniques such as Principal Component Analysis and tSNE 

Neural Networks from Shallow to Deep The notes intend to become a complete Neural Network course from the basics to some of the stateoftheart and with some applications. 

Deep Neural Network Learning: The Last Layer The notes is what I presented in the third Deep Learning Sydney Meetup, including Softmax, Centre, Linear, Contrastive, Triplet loss functions. 

Deep Reinforcement Learning new The notes is what I presented in the May 2017 Deep Learning Sydney Meetup, mainly explaining Google Deepmind’s Deep QLearning 
Machine Learning Course for Researcher and PhD students
I started to develop this machine learning course since 2009, it’s constantly being updated. I enjoy sharing my knowledge with other researchers and industry practitioners. These notes are written in English.
大家好，从2015年10月开始，我为国内的同行，同学们做了一系列概率机器学习的视频公开课。我每星期都会更新。为了方便大家，我是用中文讲的. The English version of my machine learning MOOCS is also on its way…
Probabilistic Machine Learning
Statistical Inference

Introduction to Monte Carlo methods Overview of Sampling techniques, including Rejection, Adaptive Rejection, Importance, Markov Chain Monte Carlo (MCMC), Gibbs, Bootstrap Particle Filter, and Auxiliary Particle Filter 

Stochastic matrices Stochastic matrices, Power Method Convergence and Page Rank 

Markov Chain Monte Carlo Markov Chain Monte Carlo (MCMC), Metropolis Hasting, Gibbs, Slice Sampling, Collapsed Gibbs, SwendsenWang, Elliptical Slice Sampling, (Collapsed) Gibbs sampling examples on LDA 

Sequential Monte Carlo and Particle Filter Bootstrap Particle Filter and Auxiliary Particle Filter 

Variational Inference Explain Variational Bayes in two parts: nonexponential and exponential family distribution plus stochastic variational inference. 
Statistical Models
