Subject areas include:

-Algorithms: Active Learning; Adaptive Data Analysis; AutoML; Bandit Algorithms; Boosting and Ensemble Methods; Classification; Clustering; Collaborative Filtering; Components Analysis (e.g., CCA, ICA, LDA, PCA); Density Estimation; Dynamical Systems; Kernel Methods; Large Margin Methods; Metric Learning; Missing Data; Model Selection and Structure Learning; Multitask and Transfer Learning; Nonlinear Dimensionality Reduction and Manifold Learning; Online Learning; Ranking and Preference Learning; Regression; Relational Learning; Representation Learning; Semi-Supervised Learning; Similarity and Distance Learning; Sparse Coding and Dimensionality Expansion; Sparsity and Compressed Sensing; Spectral Methods; Stochastic Methods; Structured Prediction; Unsupervised Learning.

-Applications: Activity and Event Recognition; Audio and Speech Processing; Body Pose, Face, and Gesture Analysis; Communication- or Memory-Bounded Learning; Computational Biology and Bioinformatics; Computational Photography; Computational Social Science; Computer Vision; Denoising; Dialog- or Communication-Based Learning; Fairness, Accountability, and Transparency; Game Playing; Hardware and Systems; Image Segmentation; Information Retrieval; Matrix and Tensor Factorization; Motor Control; Music Modeling and Analysis; Natural Language Processing; Natural Scene Statistics; Network Analysis; Object Detection; Object Recognition; Privacy, Anonymity, and Security; Quantitative Finance and Econometrics; Recommender Systems; Robotics; Signal Processing; Source Separation; Speech Recognition; Sustainability; Systems Biology; Text Analysis; Time Series Analysis; Tracking and Motion in Video; Video Analysis; Video Segmentation; Visual Features; Visual Question Answering; Visual Scene Analysis and Interpretation; Web Applications and Internet Data.

-Data, Competitions, Implementations, and Software: Benchmarks; Competitions or Challenges; Data Sets or Data Repositories; Software Toolkits.

-Deep Learning: Adversarial Networks; Attention Models; Biologically Plausible Deep Networks; CNN Architectures; Deep Autoencoders; Efficient Inference Methods; Efficient Training Methods; Embedding Approaches; Few-Shot Learning Approaches; Generative Models; Interaction-Based Deep Networks; Memory-Augmented Neural Networks; Meta-Learning; Neural Abstract Machines; Optimization for Deep Networks; Predictive Models; Program Induction; Recurrent Networks; Supervised Deep Networks; Virtual Environments; Visualization or Exposition Techniques for Deep Networks.

-Neuroscience and Cognitive Science: Auditory Perception; Brain Imaging; Brain Mapping; Brain Segmentation; Brain--Computer Interfaces and Neural Prostheses; Cognitive Science; Connectomics; Human or Animal Learning; Language for Cognitive Science; Memory; Neural Coding; Neuropsychology; Neuroscience; Perception; Plasticity and Adaptation; Problem Solving; Reasoning; Spike Train Generation; Synaptic Modulation; Visual Perception.

-Optimization: Combinatorial Optimization; Convex Optimization; Non-Convex Optimization; Submodular Optimization.

-Probabilistic Methods: Bayesian Nonparametrics; Bayesian Theory; Belief Propagation; Causal Inference; Distributed Inference; Gaussian Processes; Graphical Models; Hierarchical Models; Latent Variable Models; MCMC; Topic Models; Variational Inference.

-Reinforcement Learning and Planning: Decision and Control; Exploration; Hierarchical RL; Markov Decision Processes; Model-Based RL; Multi-Agent RL; Navigation; Planning; Reinforcement Learning.

-Theory: Competitive Analysis; Computational Complexity; Control Theory; Frequentist Statistics; Game Theory and Computational Economics; Hardness of Learning and Approximations; Information Theory; Large Deviations and Asymptotic Analysis; Learning Theory; Regularization; Spaces of Functions and Kernels; Statistical Physics of Learning.