Classification of Machine Learning Algorithms

[Classification] (Supervised)	[Regression] (Supervised)	[Clustering] (Unsupervised)	[Q-Learning] (Reinforcement)
Naive Bayes	Generalized Linear Models	K-Means, Fuzzy Means	Policy Gradient
Support Vector Machines	Logistic Regression	Gaussian Mixture	Trust Region Policy Optimization
K-Nearest Neighborhood	Support Vector Regression, Gaussian Process Regression	Hidden Markov Model	Proximal Policy Optimization
Decision Trees, Random Forest	Ensemble Methods	Spectral Clustering	Hindsight Experience Replay
Neural Network	Neural Network	Neural Network	Deep Q Neural Network

Artificial Intelligence on HackerRank

Chapter	Subject	Section	Exercise
1	What is AI?	I. How should we define AI?	Exercise 1: Is this AI or not?
1	What is AI?	II. Related fields	Exercise 2: Taxonomy of AI, Exercise 3: Examples of tasks
1	What is AI?	III. Philosophy of AI	Exercise 4: Definitions, definitions
2	AI problem solving	I. Search and problem solving	Exercise 5: A smaller rowboat, Exercise 6: The Towers of Hanoi
2	AI problem solving	II. Solving problems with AI	-
2	AI problem solving	III. Search and games	Exercise 7: Why so pessimistic, Max?
3	Real world AI	I. Odds and probability	Exercise 8: Probabilistic forecasts, Exercise 9: Odds
3	Real world AI	II. The Bayes rule	Exercise 10: Bayes rule (part 1), Exercise 11: Bayes rule (part 2)
3	Real world AI	III. Naive Bayes classification	Exercise 12: One word spam filter, Exercise 13: Full spam filter
4	Machine learning	I. The types of machine learning	-
4	Machine learning	II. The nearest neighbor classifier	Exercise 14: Customers who bought similar products, Exercise 15: Filter Bubbles
4	Machine learning	III. Regression	---
5	Neural networks	I. Neural network basics	---
5	Neural networks	II. How neural networks are built	---
5	Neural networks	III. Advanced neural network techniques	---
6	Implications	I. About predicting the future	---
6	Implications	II. The societal implications of AI	---
6	Implications	III. Summary	---