machine learning ml
Stanford University
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Class notes
More on Multivariate Gaussians
1 Definition 2 Gaussian facts 3 Closure properties 4 Summary 5 Exercise
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Summary
Neural Networks
PopularDeep Learning Supervised learning with non linear models Logistic Regression Neural Networks computational power data available algorithms Propagation equation
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Package deal
machine learning course
stanford machine learning course (half course + summary)
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Presentation
Python/Numpy Tutorial.
Text editor/IDE options.. (don’t settle with notepad) • PyCharm (IDE) • Visual Studio Code (IDE) • Sublime Text (IDE) • Atom • Notepad ++/gedit • Vim (for Linux)
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Class notes
Neural Networks
Deep Learning Supervised Learning with Non-linear Models Neural Networks Backpropagation Vectorization Over Training Examples
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Summary
Kernels, SVM.
summary of Kernel Methods SVMs
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Class notes
Kernel Methods
Kernels. SVM.
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Presentation
Probability Theory
Outline 1 Basics 2 Random Variables 3 Expectation-Variance 4 Joint Distributions 5 Covariance 6 RV Conditionals 7 Random Vectors 8 Multivariate Gaussian
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Class notes
The Multivariate Gaussian Distribution
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
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Class notes
Probability Theory Review
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...
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Summary
Naive Bayes, Laplace Smoothing summary
Outline Naive Bayes Laplacesmoothing Event Models Kernel Methods
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Summary
Gaussian discriminant analysis. Naive Bayes.
Gaussian discriminant analysis & it is model Naive Bayes.
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Class notes
Gaussian discriminant analysis. Naive Bayes.Laplace Smoothing.
Generative Learning algorithms Gaussian discriminant analysis. Naive Bayes. Laplace Smoothing.
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