Clever Algorithms: Nature-Inspired Programming Recipes

Easy-to-understand, with a few algorithms which i had some difficulty doing implementation in the past.

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: Intro to Hadoop and MapReduce

Udacity Link: Intro to Hadoop and MapReduce

Very easy-to-learn course (should be able to finish the tutorial and course in around 1.5 hours), basically the user will learn how to write command lines to interact with Hadoop DFS and write simple mapper and reducer python scripts to process files in Hadoop DFS

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