CS229: Machine Learning

It is time to remember the basics!

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include:
- Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- Unsupervised learning (clustering, dimensionality reduction, kernel methods);
- Learning theory (bias/variance tradeoffs, practical advice);
- Reinforcement learning and adaptive control.

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Links:
- Lecture videos
- Lecture notes
- Course materials
- Main page for the course
- Cheatsheets

Navigational tags: #armknowledgesharing #armcourses
General tags: #machinelearning #supervisedlearning #neuralnetworks #svm #unsupervisedlearning #clustering #kernel #kernel #bias #variance #tradeoff #reinforcementlearning #cheatsheet #data #learning #patternrecognition #datamining

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CS 229 ― Machine Learning Cheatsheet

Set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class.

They can (hopefully!) be useful to all future students of this course, as well as to anyone else interested in Machine Learning.

Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #supervisedlearning #unsupervisedlearning #deeplearning #tips #tricks #statistics #probability #calculus

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