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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).
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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. |
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Intro to Bayesian Statistics Explain common probability concepts such as marginal, conditional distribution and Bayes formula etc |
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Optimization Going through the basic idea behind iterative algorithms such as Stochastic Gradient Descend |
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Regression Linear, Polynomial, Mixed Effects and some classification algorithm bear the name of regression: multinomial regression |
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Recommendation System collaborative Filtering, non-negative factorization, (using multiplicative rule) |
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Dimensional Reduction Dimensionality Reduction techniques such as Principal Component Analysis and t-SNE |
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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. |
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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. |
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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…
Probabilistic Machine Learning
Statistical Inference
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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 |
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Stochastic matrices Stochastic matrices, Power Method Convergence and Page Rank |
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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 |
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Sequential Monte Carlo and Particle Filter Bootstrap Particle Filter and Auxiliary Particle Filter |
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Variational Inference Explain Variational Bayes in two parts: non-exponential and exponential family distribution plus stochastic variational inference. |
Statistical Models
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