Homework #6 - Taxonomy of a Machine

Machine Learning Algorithms

Paradigm	Learning rule	Architecture	Learning algorithm	Task
Supervised	Error-correction	Single or multilayer perceptron	Perceptron rule, Stochastic gradient descent, Back propagation, BP+reinf, SBPI	Pattern Classification, Functions Approximation, Prediction, Control
		Convolutional Networks	Stochastic gradient descent, Back propagation	Classification, Computer Vision, Speech recognition
		Auto-encoders	Gradient Descent	Data compression, Preprocessing
	Competitive	Competitive	Learning Vector Quantization	Within Class Categorization, Data Compression
Unsupervised	Hebbian	Recurrent	Hebb rule	Denoising, Attractor Learning
		Hopfield Network	Hebb rule, Inference (BP, TAP, Montecarlo)	Memory, Denoising, Attractor Learning
	Boltzmann	Boltzamann Machine	Contrastive divergence, Statistical Inference	Feature Learning, Preprocessing, Denoising, Generative model
	Kohonen's SOM	Kohonen's SOM	Kohonen's SOM	Data Analysis

 

Supervised VS Unsupervised

Data can be labeled or not