悉尼科大徐亦达教授：1000+页机器学习讲义，32 份主题推介

【新智元导读】悉尼科大徐亦达教授机器学习讲义，总共涵盖 32 个主题,1000+页讲义，包括Softmax算法、传统GAN，W-GAN数学，贝叶斯GAN, 蒙托卡罗树搜索，alphaGo学习算法等。

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

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 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

• 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]

• 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

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