学习NLP,AI,Deep Learning 的牛逼的教程

1.Andrew Moore。卡内基梅隆计算机学院的院长大大。这些基本上涵盖了很多的数据挖掘topic。

Decision Trees
Information Gain
Probability for Data Miners
Probability Density Functions
Gaussians
Maximum Likelihood Estimation
Gaussian Bayes Classifiers
Cross-Validation
Neural Networks
Instance-based learning (aka Case-based or Memory-based or non-parametric)
Eight Regression Algorithms
Predicting Real-valued Outputs: An introduction to regression
Bayesian Networks
Inference in Bayesian Networks (by Scott Davies and Andrew Moore)
Learning Bayesian Networks
A Short Intro to Naive Bayesian Classifiers
Short Overview of Bayes Nets
Gaussian Mixture Models
K-means and Hierarchical Clustering
Hidden Markov Models
VC dimension
Support Vector Machines
PAC Learning
Markov Decision Processes
Reinforcement Learning
Biosurveillance: An example
Elementary probability and Naive Bayes classifiers
Spatial Surveillance
Time Series Methods
Game Tree Search Algorithms, including Alpha-Beta Search
Zero-Sum Game Theory
Non-zero-sum Game Theory
Introductory overview of time-series-based anomaly detection algorithms
AI Class introduction
Search Algorithms
A-star Heuristic Search
Constraint Satisfaction Algorithms, with applications in Computer Vision and Scheduling
Robot Motion Planning
HillClimbing, Simulated Annealing and Genetic Algorithms

2. 

斯坦福大学在三月份开设了一门“深度学习与自然语言处理”的课程：CS224d: Deep Learning for Natural Language Processing，授课老师是青年才俊 Richard
Socher，他本人是德国人，大学期间涉足自然语言处理，在德国读研时又专攻计算机视觉，之后在斯坦福大学攻读博士学位，拜师NLP领域的巨牛 Chris
Manning 和 Deep Learning 领域的巨牛Andrew
Ng，其博士论文是《Recursive Deep Learning for Natural Language Processing
and Computer Vision》，也算是多年求学生涯的完美一击。毕业后以联合创始人及CTO的身份创办了MetaMind，作为AI领域的新星创业公司，MetaMind创办之初就拿了800万美元的风投，值得关注。

回到这们课程CS224d,其实可以翻译为“面向自然语言处理的深度学习（Deep Learning for Natural Language Processing）”，这门课程是面向斯坦福学生的校内课程，不过课程的相关材料都放到了网上，包括课程视频，课件，相关知识，预备知识，作业等等，相当齐备。课程大纲相当有章法和深度，从基础讲起，再讲到深度学习在NLP领域的具体应用，包括命名实体识别，机器翻译，句法分析器，情感分析等。Richard
Socher此前在ACL 2012和NAACL 2013 做过一个Tutorial，Deep
Learning for NLP (without Magic)，感兴趣的同学可以先参考一下: Deep
Learning for NLP (without Magic) – ACL 2012 Tutorial – 相关视频及课件 。另外，由于这门课程的视频放在Youtube上，@爱可可-爱生活 老师维护了一个网盘链接：http://pan.baidu.com/s/1pJyrXaF ，同步更新相关资料，可以关注。
课程主页链接http://cs224d.stanford.edu/syllabus.html

Event	Date	Description	Course Materials
Lecture	Mar 30	Intro to NLP and Deep Learning	Suggested Readings: [Linear Algebra Review] [Probability Review] [Convex Optimization Review] [More Optimization (SGD) Review] [From Frequency to Meaning: Vector Space Models of Semantics] [Lecture Notes 1]  [python tutorial] [slides] [video]
Lecture	Apr 1	Simple Word Vector representations: word2vec, GloVe	Suggested Readings: [Distributed Representations of Words and Phrases and their Compositionality] [Efficient Estimation of Word Representations in Vector Space] [slides] [video]
Lecture	Apr 6	Advanced word vector representations: language models, softmax, single layer networks	Suggested Readings: [GloVe: Global Vectors for Word Representation] [Improving Word Representations via Global Context and Multiple Word Prototypes] [Lecture Notes 2]  [slides] [video]
Lecture	Apr 8	Neural Networks and backpropagation -- for named entity recognition	Suggested Readings: [UFLDL tutorial] [Learning Representations by Backpropogating Errors] [slides] [video]
Lecture	Apr 13	Project Advice, Neural Networks and Back-Prop (in full gory detail)	Suggested Readings: [Natural Language Processing (almost) from Scratch] [A Neural Network for Factoid Question Answering over Paragraphs] [Grounded Compositional Semantics for Finding and Describing Images with Sentences] [Deep Visual-Semantic Alignments for Generating Image Descriptions] [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank] [(NEW) Lecture Notes 3]  [slides] [video]
Lecture	Apr 15	Practical tips: gradient checks, overfitting, regularization, activation functions, details	Suggested Readings: [Practical recommendations for gradient-based training of deep architectures] [UFLDL page on gradient checking] [slides] [video]
A1 Due	Apr 16	Assignment #1 due	[Pset 1]
Lecture	Apr 20	Recurrent neural networks -- for language modeling and other tasks	Suggested Readings: [Recurrent neural network based language model] [Extensions of recurrent neural network language model] [Opinion Mining with Deep Recurrent Neural Networks] [(NEW) Lecture Notes 4]  [slides] [video] [minimal net example (karpathy)] [vanishing grad example] [vanishing grad notebook]
Proposal due	Apr 21	Course Project Proposal due	[proposal description]
Lecture	Apr 22	GRUs and LSTMs -- for machine translation	Suggested Readings: [Long Short-Term Memory] [Gated Feedback Recurrent Neural Networks] [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling] [slides] [video]
Lecture	Apr 27	Recursive neural networks -- for parsing	Suggested Readings: [Parsing with Compositional Vector Grammars] [Subgradient Methods for Structured Prediction] [Parsing Natural Scenes and Natural Language with Recursive Neural Networks] [slides] [video]
Lecture	Apr 29	Recursive neural networks -- for different tasks (e.g. sentiment analysis)	Suggested Readings: [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank] [Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection] [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks] [slides] [video]
A2 Due	Apr 30	Pset #2 Due date	[Pset #2]
Lecture	May 4	Review Session for Midterm	Suggested Readings: N/A [slides] [video - see Piazza]
Midterm	May 6	In-class midterm
Lecture	May 11	Guest Lecture with Jason Weston from Facebook: Neural Models with Memory -- for question answering	Suggested Readings: [Memory Networks] [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks] [slides] [video]
Milestone	May 13	Course Project Milestone	[milestone description]
Lecture	May 13	Convolutional neural networks -- for sentence classification	Suggested Readings: [A Convolutional Neural Network for Modelling Sentences] [slides] [video]
Lecture	May 18	Guest Lecture with Andrew Maas: Speech recognition	Suggested Readings: [ Deep Neural Networks for Acoustic Modeling in Speech Recognition] [slides] [video]
Lecture	May 20	Guest Lecture with Elliot English: Efficient implementations and GPUs	Suggested Readings: [] [slides] [video]
A3 Due	May 21	Pset #3 Due date	[Pset #3]
Lecture	May 27	Applications of Deep Learning to Natural Language Processing	Suggested Readings: [] [slides] [video]
Lecture	Jun 1	The future of Deep Learning for NLP: Dynamic Memory Networks	Suggested Readings: [Ask me anthing: Dynamic Memory Networks for NLP] [slides] [no video]
Poster Presentation	Jun 3	Final project poster presentations: 2-5 pm, Gates patio
Final Project Due	Jun 8	Final course project due date	[project description]

3. coursera上面的Andrew NG的machine learning(https://www.coursera.org/learn/machine-learning)
以及

Geoffrey Hinton的nerual network used in Machine learning.(https://www.coursera.org/course/neuralnets)