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## 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