machine learning course

Perception Exponential Family Generalized Linear Models Soft max Regression Multiclass Classification

Contents 1 Basic Concepts and Notation 2 1.1 Basic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Matrix Multiplication 3 2.1 Vector-Vector Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Matrix-Vector Products . . . . . . . . . . . ....

Outline 1 Basic Concepts and Notation 2 Matrix Multiplication 3 Operations and Properties 4 Matrix Calculus

Generative Learning algorithms Gaussian discriminant analysis. Naive Bayes. Laplace Smoothing.

Gaussian discriminant analysis & it is model Naive Bayes.

Outline Naive Bayes Laplacesmoothing Event Models Kernel Methods

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 probabili...

a multivariate normal (or Gaussian) distribution 1 Relationship to univariate Gaussians 2 The covariance matrix 3 The diagonal covariance matrix case 4 Isocontours 5 Linear transformation interpretation

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

Outline 1 Basics 2 Random Variables 3 Expectation-Variance 4 Joint Distributions 5 Covariance 6 RV Conditionals 7 Random Vectors 8 Multivariate Gaussian

Kernels. SVM.

summary of Kernel Methods SVMs

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

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