machine learning ml

Text editor/IDE options.. (don’t settle with notepad) • PyCharm (IDE) • Visual Studio Code (IDE) • Sublime Text (IDE) • Atom • Notepad ++/gedit • Vim (for Linux)

Deep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation

Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples

summary of Kernel Methods SVMs

Kernels. SVM.

1 Definition 2 Gaussian facts 3 Closure properties 4 Summary 5 Exercise

Probability theory is the study of uncertainty. Through this class, we will be relying on concepts from probability theory for deriving machine learning algorithms. These notes attempt to cover the basics of probability theory at a level appropriate for CS 229. The mathematical theory of probability is very sophisticated, and delves into a branch of analysis known as measure theory. In these notes, we provide a basic treatment of probability that does not address these finer details. 1 Elem...

Outline Naive Bayes Laplacesmoothing Event Models Kernel Methods

Gaussian discriminant analysis & it is model Naive Bayes.