AREA 1: THEORY AND METHODS

• Exact and Approximate Inference

• Density Estimation

• Bayesian Models

• Gaussian Processes

• Model Selection

• Graphical and Graph-based Models

• Missing Data

• Ensemble Methods

• Neural Networks

• Kernel Methods

• Large Margin Methods

• Classification

• Regression

• Sparsity

• Feature Selection and Extraction

• Spectral Methods

• Embedding and Manifold Learning

• Similarity and Distance Learning

• Matrix Factorization

• Clustering

• ICA, PCA, CCA and other Linear Models

• Fuzzy Logic

• Active Learning

• Cost-sensitive Learning

• Incremental Learning

• On-line Learning

• Structured Learning

• Multi-agent Learning

• Multi-instance Learning

• Reinforcement Learning

• Instance-based Learning

• Knowledge Acquisition and Representation

• Meta Learning

• Multi-strategy Learning

• Case-Based Reasoning

• Inductive Learning

• Computational Learning Theory

• Cooperative Learning

• Evolutionary Computation

• Information Retrieval and Learning

• Hybrid Learning Algorithms

• Planning and Learning

• Convex Optimization

• Stochastic Methods

• Combinatorial Optimization

• Multiclassifier Fusion

AREA 2: APPLICATIONS

• Natural Language Processing

• Information Retrieval

• Ranking

• Web Applications

• Economics, Business and Forecasting Applications

• Bioinformatics and Systems Biology

• Audio and Speech Processing

• Signal Processing

• Image Understanding

• Sensors and Early Vision

• Motion and Tracking

• Image-based Modelling

• Shape Representation

• Object Recognition

• Video Analysis

• Medical Imaging

• Learning and Adaptive Control

• Perception

• Learning in Process Automation

• Learning of Action Patterns

• Virtual Environments

• Robotics

• Biometrics