Machine Learning By Prof. Andrew Ng :star2::star2::star2::star2::star:

This page continas all my coursera machine learning courses and resources :book: by Prof. Andrew Ng :man:

Table of Contents

1. Breif Intro
2. Video lectures Index
3. Programming Exercise Tutorials
4. Programming Exercise Test Cases
5. Useful Resources
6. Schedule
7. Extra Information
8. Online E-Books
9. Aditional Information

Breif Intro

The most of the course talking about hypothesis function and minimising cost funtions

Hypothesis

A hypothesis is a certain function that we believe (or hope) is similar to the true function, the target function that we want to model. In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails.

Cost Function

The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. The closer our hypothesis matches the training examples, the smaller the value of the cost function. Theoretically, we would like J(θ)=0

Gradient Descent

Gradient descent is an iterative minimization method. The gradient of the error function always shows in the direction of the steepest ascent of the error function. Thus, we can start with a random weight vector and subsequently follow the negative gradient (using a learning rate alpha)

Differnce between cost function and gradient descent functions

<table> <colgroup> <col width="50%" /> <col width="50%" /> </colgroup> <thead> <tr class="header"> <th> Cost Function </th> <th> Gradient Descent </th> </tr> </thead> <tbody> <tr valign="top"> <td markdown="span"> <pre><code> function J = computeCostMulti(X, y, theta) m = length(y); % number of training examples J = 0; predictions = X*theta; sqerrors = (predictions - y).^2; J = 1/(2*m)* sum(sqerrors); end </code></pre> </td> <td markdown="span"> <pre><code> function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters) m = length(y); % number of training examples J_history = zeros(num_iters, 1); for iter = 1:num_iters predictions = X * theta; updates = X' * (predictions - y); theta = theta - alpha * (1/m) * updates; J_history(iter) = computeCostMulti(X, y, theta); end end </code></pre> </td> </tr> </tbody> </table>

Bias and Variance

When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". There is a tradeoff between a model's ability to minimize bias and variance. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting.

Source: http://scott.fortmann-roe.com/docs/BiasVariance.html

Hypotheis and Cost Function Table

| Algorithem | Hypothesis Function | Cost Function | Gradient Descent | |-------------------------------------------- |----------------------------------------------------------------------- |------------------------------------------------------------------------------- |--------------------------------------------------------------------------------------- | | Linear Regression | | | | | Linear Regression with Multiple variables | | | | | Logistic Regression | | | | | Logistic Regression with Multiple Variable | | | | | Nural Networks | | | | |

Regression with Pictures

• Linear Regression
• Logistic Regression

Video lectures Index

https://class.coursera.org/ml/lecture/preview

Programming Exercise Tutorials

https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA

Programming Exercise Test Cases

https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w

Useful Resources

https://www.coursera.org/learn/machine-learning/resources/NrY2G

Schedule:

Week 1 - Due 07/16/17:

• Welcome - pdf - ppt
• Linear regression with one variable - pdf - ppt
• Linear Algebra review (Optional) - pdf - ppt
• Lecture Notes
• Errata

Week 2 - Due 07/23/17:

• Linear regression with multiple variables - pdf - ppt
• Octave tutorial pdf
• Programming Exercise 1: Linear Regression - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

Week 3 - Due 07/30/17:

• Logistic regression - pdf - ppt
• Regularization - pdf - ppt
• Programming Exercise 2: Logistic Regression - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

Week 4 - Due 08/06/17:

• Neural Networks: Representation - pdf - ppt
• Programming Exercise 3: Multi-class Classification and Neural Networks - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

Week 5 - Due 08/13/17:

• Neural Networks: Learning - pdf - ppt
• Programming Exercise 4: Neural Networks Learning - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

Week 6 - Due 08/20/17:

• Advice for applying machine learning - pdf - ppt
• Machine learning system design - pdf - ppt
• Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance - pdf - Problem - Solution
• Lecture Notes
• Errata
• Program Exercise Notes

Week 7 - Due 08/27/17:

• Support vector machines - pdf - ppt
• Programming Exercise 6: Support Vector Machines - pdf - Problem - Solution
• Lecture Notes
• Errata
• [Program Exercise Notes](/home/week-7