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:
K-Nearest Neighbors (KNN)
Binary Classification Evaluation Metrics
K-Means/Medians/Medoids Clustering
Hierarchical Agglomerative Clustering (HAC)
Expectation Maximization (EM)
Vector Space Model (tf-idf)
Decision Tree (ID3,C4.5,CART)
Random Forest (and Boosted Aggregating BAG)
Discrete Adaptive Boosting (AdaBoost)
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)