machine learning ml
Stanford University
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machine learning course
- Package deal • 16 items • 2022
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stanford machine learning course (half course + summary)
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Python/Numpy Tutorial.
- Presentation • 39 pages • 2022
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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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Neural Networks
- Summary • 6 pages • 2022
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Deep Learning 
Supervised learning with non linear models 
Logistic Regression 
Neural Networks 
computational power 
data available 
algorithms 
Propagation equation
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Neural Networks
- Class notes • 21 pages • 2022
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- Free
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Deep Learning 
Supervised Learning with Non-linear Models 
Neural Networks 
Backpropagation 
Vectorization Over Training Examples
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Kernels, SVM.
- Summary • 8 pages • 2022
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summary of Kernel Methods 
SVMs
And that's how you make extra money
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Kernel Methods
- Class notes • 30 pages • 2022
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Kernels. SVM.
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Probability Theory
- Presentation • 100 pages • 2022
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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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More on Multivariate Gaussians
- Class notes • 11 pages • 2022
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1 Definition 
2 Gaussian facts 
3 Closure properties 
4 Summary 
5 Exercise
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The Multivariate Gaussian Distribution
- Class notes • 10 pages • 2022
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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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Probability Theory Review
- Class notes • 12 pages • 2022
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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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