ACM Computing Classification System

Artificial Intelligence
Natural language processing [Ch. 23, 24]
Information extraction  [Sec. 23.6]
Machine translation [Sec. 23.6, 24.6]
Discourse, dialogue and pragmatics [Sec. 23.5]
Natural language generation [Sec. 23.6]
Speech recognition [Sec. 23.6]
Lexical semantics [Sec. 23.4, 24.1]
Phonology / morphology [Sec. 23.1]
Language resources [Sec. 24.6]

Knowledge representation and reasoning [Ch. 10]
Description logics [Sec. 10.5]
Semantic networks [Sec. 10.5]
Nonmonotonic, default reasoning and belief revision [Sec. 10.6]
Probabilistic reasoning [Ch. 13-15]
Vagueness and fuzzy logic [Sec. 23.1]
Causal reasoning and diagnostics [Sec. 13.5]
Temporal reasoning [Ch. 14]
Cognitive robotics [Sec. 26.8]
Ontology engineering [Sec. 10.1]
Logic programming and answer set programming [Sec. 9.4]
Spatial and physical reasoning [Sec. 10.6]
Reasoning about belief and knowledge [Sec. 10.4]

Planning and scheduling [Ch. 11]
Planning for deterministic actions [Sec. 11.1, 11.2]
Planning under uncertainty [Sec. 11.5]
Multi-agent planning [Ch. 18]
Planning with abstraction and generalization [Sec. 11.3, 11.4]
Robotic planning [Sec. 26.5]
    Evolutionary robotics [Sec. 4.1]

Search methodologies [Ch. 3, 4]
Heuristic function construction [Sec. 3.5]
Discrete space search [Ch. 3]
Continuous space search [Sec. 4.2]
Randomized search [Sec. 19.8]
Game tree search [Ch. 5]
Abstraction and micro-operators [Sec. 3.6]
Search with partial observations [Sec. 4.4]

Control methods [Ch. 26]
Robotic planning [Sec. 26.5]
    Evolutionary robotics [Sec. 4.1]
Computational control theory [Sec. 26.5]
Motion path planning [Sec. 26.5]

Philosophical/theoretical foundations of artificial intelligence [Ch. 27]
Cognitive science [Sec. 1.1]
Theory of mind [Sec. 27.2]

Distributed artificial intelligence [Ch. 18]
Multi-agent systems [Ch. 18]
Intelligent agents [Ch. 2]
Mobile agents [Ch. 26]
Cooperation and coordination [Sec. 18.3]

Computer vision [Ch. 25]
Computer vision tasks [Sec. 25.7]
Image and video acquisition [Sec. 25.3, 25.6]
Computer vision representations [Sec. 25.3]
Computer vision problems [Sec. 25.7]


Machine learning [Ch. 19-22]
Supervised learning [Ch. 19]
    Ranking [Sec. 18.4]
    Supervised learning by classification [Sec. 19.6]
    Supervised learning by regression [Sec. 19.6]
    Structured outputs [Ch. 19]
    Cost-sensitive learning [Sec. 22.3]
Unsupervised learning [Sec. 20.3]
    Cluster analysis [Sec. 20.3]
    Anomaly detection [Sec. 19.9]
    Mixture modeling [Sec. 20.2]
    Topic modeling [Ch. 23 Notes]
    Source separation [N/A]
    Motif discovery [N/A]
    Dimensionality reduction and manifold learning [Sec. 21.7]
Reinforcement learning [Ch. 22]
    Sequential decision making [Ch. 17]
    Inverse reinforcement learning [Sec. 22.6]
    Apprenticeship learning [Sec. 22.6]
    Multi-agent reinforcement learning [Sec. 22.7]
    Adversarial learning [Sec. 21.7]
Multi-task learning [Sec. 21.7]
    Transfer learning [Sec. 21.7]
    Lifelong machine learning [Ch. 4 Notes]
    Learning under covariate shift [Sec. 19.9]
Learning settings [Sec. 19.8]
    Batch learning [Sec. 21.4]
    Online learning settings [Sec. 19.8]
    Learning from demonstrations [Sec. 22.6]
    Learning from critiques [Sec. 2.4]
    Learning from implicit feedback [Sec. 2.4]
    Active learning settings [Sec. 22.3]
    Semi-supervised learning settings [Sec. 19.9]
Machine learning approaches [Ch. 19]
    Classification and regression trees [Sec. 19.3]
    Kernel methods [Sec. 19.7]
        Support vector machines [Sec. 19.7]
            Gaussian processes [Sec. 20.3]
            Neural networks [Ch. 21]
    Logical and relational learning [Sec. 19.7]
    Inductive logic learning [Sec. 19.2]
    Statistical relational learning [Ch. 20]
    Learning in probabilistic graphical models [Ch. 20]
        Maximum likelihood modeling [Sec. 20.2]
        Maximum entropy modeling [Ch. 20]
        Maximum a posteriori modeling [Sec. 20.1]
        Mixture models [Sec. 20.3]
        Latent variable models [Sec. 20.3]
        Bayesian network models [Ch. 20]
    Learning linear models [Sec. 19.6]
        Perceptron algorithm [Ch. 21 Notes]
    Factorization methods [Sec. 19.9]
        Non-negative matrix factorization [N/A]
            Factor analysis [Sec. 19.9]
            Principal component analysis [Sec. 21.7]
            Canonical correlation analysis [N/A]
            Latent Dirichlet allocation [Ch. 23 Notes]
Rule learning [Ch. 22 Notes]
Instance-based learning [Sec. 19.7]
Markov decision processes [Sec. 17.1]
Partially-observable Markov decision processes [Sec. 17.4]
Stochastic games [Sec. 18.2]
Learning latent representations [Sec. 20.3]
    Deep belief networks [Ch. 21]
Bio-inspired approaches [Ch. 4 Notes]
    Artificial life [Ch. 4 Notes]
    Evolvable hardware [N/A]
    Genetic algorithms [Sec. 4.2]
    Genetic programming [Sec. 4.1]
    Evolutionary robotics [Sec. 4.1]
    Generative and developmental approaches [Sec. 20.2]
Machine learning algorithms [Ch. 19-22]
    Dynamic programming for Markov decision processes [Sec. 17.2]
    Value iteration [Sec. 17.2]
    Q-learning [Sec. 22.3]
    Policy iteration [Sec. 17.2]
    Temporal difference learning [Sec. 22.2]
    Approximate dynamic programming methods [Sec. 22.2]
    Ensemble methods [Sec. 19.8]
    Boosting [Sec. 19.8]
    Bagging [Sec. 19.8]
    Spectral methods [N/A]
    Feature selection [Sec. 19.4]
    Regularization [Sec. 19.4]
    Cross-validation [Sec. 19.4]