1. Machine Learning (ML)
1.1. Supervised Learning
1.1.1. Regression e.g
1.1.1.1. Linear
1.1.1.2. Polynomial
1.1.2. Classification e.g
1.1.2.1. KNN
1.1.2.2. Decision Trees
1.1.2.2.1. Random Forests
1.1.2.3. Linear classifiers e.g
1.1.2.3.1. Naive-Bayes
1.1.2.3.2. Logistic Regresion
1.1.2.3.3. Perceptron
1.1.2.3.4. Fisher's linear discriminant
1.1.2.3.5. Support Vector Machines (SVM)
1.1.3. other related topics
1.1.3.1. Inductive Logic Programming (ILP)
1.1.3.2. Association Rule Learning
1.2. Unsupervised Learning
1.2.1. Clustering e.g
1.2.1.1. k-means
1.2.1.2. k-medians
1.2.1.3. hierarchical clustering
1.2.2. Non Clustering
1.3. Reinforcement Learning
2. Deduction, Reasoning, Problem Solving
3. Knowledge Representation
4. Planning and scheduling
5. Perception: Computer Vision
6. Natural Language Processing (NLP)
7. Social Intelligence
8. Robotics: Motion and Manipulation
9. Expert Systems
10. Speech Recognition
11. Predictive Analytics
11.1. Regression techniques
11.1.1. Linear regression model
11.1.2. Discrete choice models
11.1.3. Logistic regression
11.1.4. Multinomial logistic regression
11.1.5. Probit regression
11.1.6. Time series models
11.1.7. Survival or duration analysis
11.1.8. Classification and regression trees (CART)
11.1.9. Multivariate adaptive regression splines (MARS)
11.2. Machine learning techniques
11.2.1. Neural networks
11.2.2. Multilayer perceptron (MLP)
11.2.3. Radial basis functions
11.2.4. Support vector machines
11.2.5. Naïve Bayes
11.2.6. k-nearest neighbours (KNN)
11.2.7. Geospatial predictive modeling