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AI by Mind Map: AI

1. Machine Learning (ML)

1.1. Supervised Learning

1.1.1. Regression e.g Linear Polynomial

1.1.2. Classification e.g KNN Decision Trees Random Forests Linear classifiers e.g Naive-Bayes Logistic Regresion Perceptron Fisher's linear discriminant Support Vector Machines (SVM)

1.1.3. other related topics Inductive Logic Programming (ILP) Association Rule Learning

1.2. Unsupervised Learning

1.2.1. Clustering e.g k-means k-medians 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

12. Neural Networks

13. Deep Learning