[edit]

Models and Methods

• Bayesian methods

• Boosting

• Causality

• Compressed sensing, sparse coding

• Deep learning

• Ensemble methods

• Fairness

• 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

• Privacy

• Representation, structure learning

• Robustness

• 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