Machine Learning with 1305

• Introduction
  • What is 1305 library about?
  • Downloading and installation
  • How to read this document
• Frequently Asked Questions
  • Running the software
  • Algorithms/Executables
  • File Formats
  • Company
  • 1305 and the other Machine Learning Libraries
• Running 1305 on the Amazon EC2 cloud
• 1305 File Format
  • Introduction
  • Simple CSV Format
  • 1305 File Specification
  • Currently Supported Combinations
• An Introduction to Machine Learning
  • Data Representation/Dimensionality
  • Classification
  • Parametric vs Non-Parametric methods in ML
  • K-Nearest Neighbor Classifier: The Simplest Classifier
  • Regression/Prediction
  • Clustering
  • Optimization in Machine Learning
• Algorithms
  • Kernel Density Estimation and Non-parametric Bayes Classifier
  • K-Means
  • Kernel Principal Components Analysis
  • Linear Regression
  • Neighbors (Nearest, Farthest, Range, k, Classification)
  • Non-Negative Matrix Factorization
  • Support Vector Machines
  • Dimensionality Reduction
  • Fast Singular Value Decomposition
  • Decision Tree
  • Hadoop based Bootstrapped SVM
• Expressing machine learning algorithms as optimization problems
  • K-Nearest Neighbors
  • Kernel Density Estimation
  • Non-Negative Matrix Factorization
  • Support Vector Machine
  • Maximum Variance Unfolding
  • Kernel Principal Component Analysis
  • K-Means
  • Linear Regression
  • LASSO
• Examples on Datasets
  • Astronomy dataset (SDSS)
  • 3 Synthetic Gaussians
  • A positive matrix
  • Netflix dataset
  • The movielens dataset
  • A uniformly random dataset
  • A synthetic dataset for regression
  • A bag of words (docword nips) document collection
  • The UCI adult dataset
• Benchmarks
  • Datasets
  • Algorithms