Machine Learning course

 By Richard Xu, Associate Professor of Machine Learning & Director, Industry Analytics & Visualisation, University of Technology Sydney, Australia

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, non-negative factorization, (using multiplicative rule)
Dimensional Reduction  Dimensionality Reduction techniques such as Principal Component Analysis and t-SNE
Neural Networks from Shallow to Deep  The notes intend to become a complete Neural Network course from the basics to some of the state-of-the-art 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 Q-Learning

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…

优酷 连接
Youtube 连接

Probabilistic Machine Learning

Probabilities and Estimations  Various common probability distribution (functions), natural parameters and Maximum Likelihood & Maximum A Posterior Estimation
Statistical Properties  A few useful statistical properties include inequalities, convergence and uniqueness
Expectation-Maximization  Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model
Continous and Discrete State Dynamic Systems  Derivations for Kalman Filter and Hidden Markov Model

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, Swendsen-Wang, 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: non-exponential and exponential family distribution plus stochastic variational inference.

Statistical Models

Baysian Non-Parametrics Basics and Inference  Dirichlet Process, Hieratical Dirichlet Process, HDP-HMM, Indian Buffet Process and their inference methodologies using Gibbs and Slice Gibbs.
Determinantal Point Processes  Explain Determinantal Point Processes in an intuiative manner.




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