Machine Learning

Supervised Learning

• Linear Methods
• Regression
• Classification
• Bayesian Methods
• Fundamentals of Bayesian Inference
• Basis expansion and Regularization
• Kernel Methods
• Model Selection
• Model Inference
• Boosting and Trees
• Neural Networks
• Support Vector Machines
• Nearest-neighbor methods

Unsupervised Learning

• Nonparametric Regression
• Association Rules
• Cluster Analysis
• Principal Component Analysis
• Random Forests
• Graphical Models

- Latend Dirichlet Allocation

High-dimensional learning

On Line Learning
Learning one instance at the time. Incremental learning with feedback.

• Online Machine Learning (Wikipedia Article)
• Wowpal Wabbit

Adversarial learning

Computational Learning Theory

Datasets

Projects

• Apache Mahout
• Weka – Java Data Mining
• Lingpipe – Java Natural Language Processing

Links

• videolectures.net

Fundamentals

• Probability Theory
• Vapnik-Chervonenkis Theory

Books

• Pattern Recognition and Machine Learning, Christopher Bishop – Excelent introduction to the field
• The Elements of Statistical Learning – Hastle, Tibshirani, FriedmanClassical text – a must-have
• Principles and Theory for Data Mining and Machine Learning – Clarke, Fokoue, Zhang