Models and Methods

• Bayesian methods
• Boosting
• Causality
• Compressed sensing, sparse coding
• Deep learning
• Ensemble methods
• Feature selection
• Frequentist methods (Maximum likelihood)
• Gaussian processes
• Graphical models
• Kernel methods
• Learning theory
• Learning on graphs
• Large margin methods
• Logic and probability
• Information geometry
• Information theory
• Matrix and tensor factorization
• Manifold learning, nonlinear embedding
• Model selection
• Nonparametric models
• Online learning
• Representation, structure learning
• Sampling
• Spatial models
• Spectral methods
• Stochastic processes
• Time series and sequence models

Problem types

Supervised learning

• Active learning
• Classification
• Prediction with missing data
• Regression
• Structured prediction

Unsupervised and semi-supervised learning

• Clustering
• Density estimation
• Dimension reduction
• Latent variable models
• Topic models

Decision-making and control

• Control theory
• Decision theory
• Game theory
• Mechanism design
• Multi-agent systems
• No-regret learning
• Planning
• Reinforcement learning

Algorithms and applications

Optimization and computation methods

• Combinatorial optimization
• Convex optimization
• Gradient-based optimization
• Monte Carlo methods
• Numerical methods

Systems and software

• High performance architectures
• Large-scale learning systems
• Parallel and distributed algorithms
• Software packages

Applications

• Biology and genomics
• Brain computer interfaces
• Cognitive science
• Collaborative filtering, recommendation systems
• Computer vision
• Data visualization
• Economics and finance
• Image processing
• Informatics
• Information retrieval
• Medical imaging
• Natural language processing, text mining
• Network data analysis
• Neuroscience
• Robotics
• Scientific computing, data analysis
• Signal processing
• Statistical databases
• World wide web, search