Online Course: Statistical Reasoning for Public Health 2 Regression Methods

While this course may be useful for persons with less technical background, but somehow i found this course is too simple for my taste.

https://www.coursera.org/course/statreasoning2

Online Course: Regression Models

The course is not as comprehensive as i thought but still worth my time for going through quickly on some of the concepts learned before.

https://www.coursera.org/course/regmods

Learning Storm

Just completes the book "Learning Storm". Very nice read, interesting readers can go to the following link to buy:

https://www.packtpub.com/big-data-and-business-intelligence/learning-storm

The book covers quite widely, with quite a number of ways to show how other technologies  working with Storm introduced in a easy-to-understand way such as the covering of ZooKeeper, Kafka, Hadoop, YARN, Ganglia, JMX, HBase, Redis, MySQL, etc. I especially likes the way they teach Trident, which makes it much easier to grasp the concept of Trident, and the last chapter on machine is extremely useful.

While the normal readers can read the book chapters by chapters to take a slow and full exposure. For someone like me, who always like to delve directly into practice, the best approach is actually to read the book three times, each time skipping some chapters.

During first time, the reader should go through chapter 1 to chapter 4, skipping the thrift library introduction in chapter 3, and then directly jump to chapter 8, which gives an example of log processing in Storm. With this the reader will build a level of confidence after practicing the simple cases in these chapters.

During the second time, the reader should go through chapter 5 and chapter 9 to get a good ideas of what Trident is and how Trident work, as well as how to do machine learning using Trident.

During the third time, the reader can optionally go through the thrift library in chapter 3, then go to chapter 7 which show rich tools to interact with Storm such JMX and Ganglia. Finally if there is a need for integration with Hadoop, then go to chapter 6 and some other parts in chapter 7.

Online Course: Machine Learning: Supervised Learning

Udacity Link: Machine Learning: Supervised Learning

Easy-to-learn course, by studying the course, I was able to implement the following algorithms for my library

• AdaBoost 
• TreeBagging
• RandomForest
• Improves some designs over my ID3 and C45 decision tree.

Online Course: Social Network Analytics

Coursera Link: Social Network Analytics

Easy-to-follow course, by studying this course, I was able to implement the following algorithm in C# for my library:

• Eigen Vector Centrality Measure
• Degree Centrality Measure
• Closeness Centrality Measure
• Betweenness Centrality Measure
• Community Detection

Online Course: Machine Learning

Coursera Link: Machine Learning

Easy-to-learn course, by studying the course, I implemented the following algorithms in C# for library:

• Linear Regression
• Linear Solver
• Logistic Regression
• MLP Neural Network with gradient descent learning
• Linear and gaussian kernel SVM
• Gaussian distribution -based Anomaly Detection algorithm
• Collaborative filtering based Recommender
• Content based Recommender
• Regularization and Error Analysis, Learning Curve, etc
• Principal Component Analysis (PCA)

Online Course: Introduction to Artificial Intelligence

Udacity Link: Intro to Artificial Intelligence

Easy-to-learn course, by studying the course, I implemented the following algorithms in C# for my library:

• Probabilistic inference: Bayesian Network and Inference
• Computer Vision: Sobel Filter (Edge Detection)
• Computer Vision: Gaussian Filter (Image Blur)
• Computer Vision: Prewitt Filter (Edge Detection)
• Computer Vision: Kirsh Filter (Edge Detection)
• Computer Vision: Linear and gradient filters
• Computer Vision: Average Filter (Noise Removal)
• Computer Vision: Fog Filter (Fog effect)
• Computer Vision: SSD / SAD and minimization / Disparity Map
• NLP: Language Identifier
• NLP: Text Segmentation
• NLP: Spell Correction

Online Course: Artificial Intelligence for Robotics

Udacity Link: Artificial Intelligence for Robotics

Easy-to-learn course, by studying the course, I implemented the following algorithms in C# for my library:

• Bayesian Filter: Histogram Filter (Uni-Variate)
• Bayesian Filter: Particle Filter (Multi-Variate)
• Bayesian Filter: Kalman Filter (Multi-Variate)
• Robotics: SLAM (Simultaneous Localization and Mapping) (Multi-Variate)
• Controller: Tweedle for PID Controller parameter tuning
• Controller: Smoothing function for path

Online Course: Machine Learning: Unsupervised Learning

Udacity Link: Machine Learning: Unsupervised Learning

Easy-to-learn course, by studying the course,  I implemented the following algorithms in C# for my library:

• Clustering: EM (Expectation Maximization) Clustering
• Clustering: Single Linkage Clustering
• Feature Selection: Forward Sequencing
• Feature Selection: Backward Elimination
• Feature Selection: Hill Climbing Feature Selection
• Feature Transformation: Principal Component Analysis
• Feature Transformation: Independent Component Analysis
• EDA Solver: MIMIC

Online Course: Machine Learning: Reinforcement Learning

Link : Udacity: "Machine Learning: Reinforcement Learning"

Easy-to-learn course for reinforcement learning. By studying the course, i implemented the following algorithms in C# for my library:

• Reinforcement Learning: Q-learning
• Reinforcement Learning: Value iteration
• Reinforcement Learning: Policy iteration
• Game: MiniMax
• Game: ExpectiMiniMax

Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management

Although from my personal opinion, the book is not very well written, it provides quite a bit of insights on how mining method such as logistic regression can be used to customer segmentation, profiling, and create various targeting, propensity models for marketing and risk as well as customer relationship management. the illustrated programming language is in SAS, but the code is not completely listed in the book. It is not a beginning book for SAS but a seasoned programmer who previously does not programme in SAS should not have difficulty picking up the code and implementation in the book. btw the book is outdated,

http://www.amazon.com/Data-Mining-Cookbook-Relationship-Management/dp/0471385646

Reading Status: Not Completed.

Programming Collective Intelligence: Building Smart Web 2.0 Application

Very easy-to-understand book on how to apply machine learning and computational intelligence methods on web data.

http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325

Status: Not Completed

Learning OpenCV Computer Vision with the OpenCV library

This is a very easy to understand book on computer vision which uses the OpenCV library to develop computer vision and learning applications.
Reading Status: Not Completed

Mining Graph Data

The book I read when researching on application of graph mining for vehicle routing problems during my PHD.

Link: http://www.amazon.com/Mining-Graph-Data-Diane-Cook/dp/0471731900
Reading Status: Completed

Constructing intelligent agents using JAVA

The first book that i came across concepts in computational intelligence method related to data mining and machine learning.

Link: http://books.google.com.sg/books/about/Constructing_intelligent_agents_using_JA.html?id=ZbBQAAAAMAAJ&redir_esc=y
Reading Status: Completed

Reinforcement Learning

This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.

Link: http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
Reading Status: Completed

The book helped me to implement the following algorithms in C# for my library:

• TD(0) Learning
• Q-Learning
• R-Learning
• Sarsa
• Q (Lambda) - Learning
• Sarsa (Lambda)