## 【悉尼科大徐亦达教授】机器学习讲义，32份主题推介

2019 年 9 月 7 日 专知

【导读】悉尼科大徐亦达教授机器学习讲义，总共涵盖32个主题。2019创新工场DeeCAMP讲义,(softmax的故事) Softmax的属性, 估计softmax时不需计算分母, 概率重新参数化, Gumbel-Max技巧和REBAR算法 传统GAN，W-GAN数学，对偶性和KKT条件，Info-GAN，贝叶斯GAN, 蒙托卡罗树搜索，alphaGo学习算法, 政策梯度定理, RL中可信区域优化的数学,TRPO自然梯度, 近似策略优化(PPO), 共轭梯度算法。

https://github.com/roboticcam/machine-learning-notes

• ### DeeCamp 2019：Story of Softmax

properties of Softmax, Estimating softmax without compute denominator, Probability re-parameterization: Gumbel-Max trick and REBAR algorithm (softmax的故事) Softmax的属性, 估计softmax时不需计算分母, 概率重新参数化, Gumbel-Max技巧和REBAR算法

• ### DeeCamp 2018：When Probabilities meet Neural Networks

Expectation-Maximization & Matrix Capsule Networks; Determinantal Point Process & Neural Networks compression; Kalman Filter & LSTM; Model estimation & Binary classifier (当概率遇到神经网络) 主题包括：EM算法和矩阵胶囊网络; 行列式点过程和神经网络压缩; 卡尔曼滤波器和LSTM; 模型估计和二分类问题关系

# Video Tutorial to these notes 视频资料

• I recorded about 20% of these notes in videos in 2015 in Mandarin (all my notes and writings are in English) You may find them on Youtube and bilibili and Youku

# Data Science 数据科学课件

• ### 30 minutes introduction to AI and Machine Learning

An extremely gentle 30 minutes introduction to AI and Machine Learning. Thanks to my PhD student Haodong Chang for assist editing

30分钟介绍人工智能和机器学习, 感谢我的学生常浩东进行协助编辑

• ### Regression methods

Classification: Logistic and Softmax; Regression: Linear, polynomial; Mix Effect model [costFunction.m] and [soft_max.m]

• ### Recommendation system

collaborative filtering, Factorization Machines, Non-Negative Matrix factorisation, Multiplicative Update Rule

• ### Dimension Reduction

classic PCA and t-SNE

• ### Introduction to Data Analytics and associate Jupyter notebook

Supervised vs Unsupervised Learning, Classification accuracy

# Deep Learning 深度学习课件

• ### Optimisation methods

Optimisation methods in general. not limited to just Deep Learning

• ### Neural Networks

basic neural networks and multilayer perceptron

• ### Convolution Neural Networks: from basic to recent Research

detailed explanation of CNN, various Loss function, Centre Loss, contrastive Loss, Residual Networks, Capsule Networks, YOLO, SSD

• ### Word Embeddings

Word2Vec, skip-gram, GloVe, Fasttext

• ### Deep Natural Language Processing

RNN, LSTM, Seq2Seq with Attenion, Beam search, Attention is all you need, Convolution Seq2Seq, Pointer Networks

• ### Mathematics for Generative Adversarial Networks

How GAN works, Traditional GAN, Mathematics on W-GAN, Duality and KKT conditions, Info-GAN, Bayesian GAN

GAN如何工作，传统GAN，W-GAN数学，对偶性和KKT条件，Info-GAN，贝叶斯GAN

• ### Restricted Boltzmann Machine

basic knowledge in Restricted Boltzmann Machine (RBM)

# Reinforcement Learning 强化学习

• ### Reinforcement Learning Basics

basic knowledge in reinforcement learning, Markov Decision Process, Bellman Equation and move onto Deep Q-Learning

• ### Monto Carlo Tree Search

Monto Carlo Tree Search, alphaGo learning algorithm

• ### Policy Gradient

Policy Gradient Theorem, Mathematics on Trusted Region Optimization in RL, Natural Gradients on TRPO, Proximal Policy Optimization (PPO), Conjugate Gradient Algorithm

# Probability and Statistics Background 概率论与数理统计基础课件

• ### Bayesian model

revision on Bayes model include Bayesian predictive model, conditional expectation

• ### Probabilistic Estimation

some useful distributions, conjugacy, MLE, MAP, Exponential family and natural parameters

• ### Statistics Properties

useful statistical properties to help us prove things, include Chebyshev and Markov inequality

# Probabilistic Model 概率模型课件

• ### Expectation Maximisation

Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model, [gmm_demo.m] and [kmeans_demo.m]and [Youku]

• ### State Space Model (Dynamic model)

explain in detail of Kalman Filter [Youku][kalman_demo.m] and Hidden Markov Model [Youku]

# Inference 推断课件

• ### Variational Inference

explain Variational Bayes both the non-exponential and exponential family distribution plus stochastic variational inference. [vb_normal_gamma.m] and [优酷链接]

• ### Stochastic Matrices

stochastic matrix, Power Method Convergence Theorem, detailed balance and PageRank algorithm

• ### Introduction to Monte Carlo

inverse CDF, rejection, adaptive rejection, importance sampling [adaptive_rejection_sampling.m] and [hybrid_gmm.m]

• ### Markov Chain Monte Carlo

M-H, Gibbs, Slice Sampling, Elliptical Slice sampling, Swendesen-Wang, demonstrate collapsed Gibbs using LDA [lda_gibbs_example.m] and [test_autocorrelation.m] and [gibbs.m] and [Youku]

• ### Particle Filter (Sequential Monte-Carlo)

Sequential Monte-Carlo, Condensational Filter algorithm, Auxiliary Particle Filter [Youku]

# Advanced Probabilistic Model 高级概率模型课件

• ### Bayesian Non Parametrics (BNP) and its inference basics

Dircihlet Process (DP), Chinese Restaurant Process insights, Slice sampling for DP [dirichlet_process.m] and [优酷链接] and [Jupyter Notebook]

• ### Bayesian Non Parametrics (BNP) extensions

Hierarchical DP, HDP-HMM, Indian Buffet Process (IBP)

• ### Determinantal Point Process

explain the details of DPP’s marginal distribution, L-ensemble, its sampling strategy, our work in time-varying DPP

-END-

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