Fun Q: A Functional Introduction to Machine Learning in Q

clone this project and start q with any of the following:

q fun.q

q plot.q

q linreg.q

q onevsall.q

q nn.q

q kmeans.q

q knn.q

q em.q

q recommend.q

q decisiontree.q

q adaboost.q

q randomforest.q

q supportvectormachine.q

q hiragana.q

you can then read the comments and run the examples one by one. topics include:

Plotting

Least Squares Regression

Gradient Descent

Logistic Regression

Binary Classification Evaluation Metrics

One vs All Logistic Regression

Neural Networks

K-Means/Medians Clustering

Hierarchical Clustering Analysis (HCA)

Expectation Maximization (EM)

K-Nearest Neighbors (kNN)

Markov Clustering Algorithm (MCL)

Naive Bayes

Decision Tree (ID3,C4.5)

Adaboost

Random Forest

Google PageRank

Content-Based Filtering (Recommender Systems)

Collaborative Filtering (Recommender Systems)

Vector Space Model (tf-idf)

Support Vector Machine (SVM)