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 kmeans.q

q knn.q

q hac.q

q em.q

q nb.q

q tfidf.q

q decisiontree.q

q adaboost.q

q randomforest.q

q linreg.q

q onevsall.q

q nn.q

q hiragana.q

q recommend.q

q pagerank.q

q supportvectormachine.q

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

Plotting

K-Nearest Neighbors (KNN)

Binary Classification Evaluation Metrics

K-Means/Medians/Medoids Clustering

Hierarchical Agglomerative Clustering (HAC)

Expectation Maximization (EM)

Naive Bayes

Vector Space Model (tf-idf)

Decision Tree (ID3,C4.5,CART)

Random Forest (and Boosted Aggregating BAG)

Discrete Adaptive Boosting (AdaBoost)

Regularization (L1,L2)

Least Squares Regression

Gradient Descent

Logistic Regression

One vs All

Neural Network Classification

Neural Network Regression

Content-Based Filtering (Recommender Systems)

Collaborative Filtering (Recommender Systems)

Google PageRank

Support Vector Machine (SVM)

Markov Clustering Algorithm (MCL)