Time and Location : Monday, Wednesday pmpm, links to lecture are on Canvas. Note : This is being updated for Spring The dates are subject to change as we figure out deadlines.
Please check back soon. Linear Regression. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models.
Naive Bayes. Laplace Smoothing. Support Vector Machines. GMM non EM. Expectation Maximization. Factor Analysis. Value function approximation. Previous projects: A list of last quarter's final projects can be found here.
Data: Here is the UCI Machine learning repositorywhich contains a large collection of standard datasets for testing learning algorithms. Slides Introduction slides [ pptx ] Introduction slides [ pdf ]. Problem Set 0. Weighted Least Squares. Class Notes [ live lecture notes ].
Problem Set 1. Class Notes Generative Algorithms. Class Notes Support Vector Machines.
Coursera: Machine Learning (Week 2) [Assignment Solution] - Andrew NG
Class Notes Deep Learning Backpropagation. Problem Set 2. Notes Evaluation Metrics. Class Notes Regularization and Model Selection.
Notes Deep Learning.
Problem Set 3. Class Notes Midterm review. Class Notes Factor Analysis.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
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Sign up. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit. Latest commit c1d Mar 22, Proof of my certification can be seen here. Note: The material provided in this repository is only for helping those who may get stuck at any point of time in the course. It is strongly advised that no one should just copy the solutions voilation of Coursera Honor Code presented here. You signed in with another tab or window.
Reload to refresh your session. You signed out in another tab or window. Feb 7, Mar 1, Mar 15, Initial commit. Feb 6, Mar 22, GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repositry contains the python versions of the programming assignments for the Machine Learning online class taught by Professor Andrew Ng.
This is perhaps the most popular introductory online machine learning class. In addition to being popular, it is also one of the best Machine learning classes any interested student can take to get started with machine learning.
The Python machine learning ecosystem has grown exponentially in the past few years, and is still gaining momentum.
I suspect that many students who want to get started with their machine learning journey would like to start it with Python also. It is for those reasons I have decided to re-write all the programming assignments in Python, so students can get acquainted with its ecosystem from the start of their learning journey.
These assignments work seamlessly with the class and do not require any of the materials published in the MATLAB assignments. Here are some new and useful features for these sets of assignments:.
You can work on the assignments in an online workspace called Deepnote. This allows you to play around with the code and access the assignments from your browser. To get started, you can start by either downloading a zip file of these assignments by clicking on the Clone or download button.
If you have git installed on your system, you can clone this repository using :. Each assignment is contained in a separate folder. For example, assignment 1 is contained within the folder Exercise1. Each folder contains two files:. We recommend using at least these versions of the required libraries or later.
Python 2 is not supported. We highly recommend using anaconda for installing python. Click here to go to Anaconda's download page. Make sure to download Python 3.
If you are on a windows machine:. Change the permission to the downloaded file so that it can be executed. So if the downloaded file name is AnacondaThanks for your comments. I still have some problems with the solutions, could you help me. In this case is with line 17, J History I tried to re-ran the code and everything worked perfectly fine with me.
Please check you code. In the code, you can variable "y" is defined in parameter list itself. So, logically you should not get that error. There must something else you might be missing outside these functions. If you got the solution please confirm here. It will be helpful for others. Hi Sasank, because small y already is used as a input argument for the mentioned functions. So, you can't get the error like y is undefined. Are you sure you haven't made any mistake like small y and Capital Y?
Please check it and try again. I was stuck for two months in Week 2 Assignment of Machine Learning. Thanx for your guidance due to which I can now understand coding in a better way and finally I have passed 2nd Week Assignment. I tried to reran the code. But i dont know where to load data. Refer the forum within the course in Coursera. They have explained the step to submit the assignments in datails. HelloIn the gradient descent.Teaching Assistant : Li-Yun Wang liyuwang pdx.
Course Mailing List: mlfall cs. Facility in at least one high-level programming language.0.1 How to submit coursera 'Machine Learning' Assignment
Main topics: : Perceptrons, neural networks, logistic regression, evaluating classifiers, support vector machines, ensemble learning, Bayesian learning, unsupervised learning, deep learning, and reinforcement learning. Textbook: No textbook. Readings will be assigned from materials available on-line.
Late homework policy: Students must request and be granted an extension on any homework assigment before the assignment is due. Quizzes: The class will have several short in-class quizzes to test basic understanding of the material presented in class and in the readings. You are allowed to bring in one double-sided page of notes for each quiz.
There will be no midterm or final exam. Final Project: Students will work in small teams people on a final project. Details will be given during class. Students with disabilities : If you are a student with a disability in need of academic accommodations, you should register with the Disability Resource Center and notify the instructor immediately to arrange for support services.
Tuesday September 25 Introduction to machine learning pptx or pdf. Reading: Chapters of Michael Nielsen's online book on neural networks covers the basics of perceptrons and multilayer neural networks.
Thursday September 27 Pre-test solutions. Multiclass classification pptx or pdf. Tuesday October 2 Multilayer neural networks pptx or pdf. Thursday October 4 Quiz 1. Thursday October 11 Support vector machines, continued.
Dimensionality reduction pptx or pdf. Evaluating classifiers pptx or pdf. Reading: : T. Thursday October 18 Quiz 2. Tuesday October 23 Bayesian learning pptx or pdf. Reading: T. Thursday October 25 Bayesian learning, continued. Solution to "In class' exercises, part 2, number 3. Logistic regression pptx or pdf.Homework 1 Corrections and Clarifications: While this correction pertains to an already completed homework, it is important to note there was an error in question 2.
The question stated that if you minimized the equation for O a,t with respect to a and t, you would find the optimal split for the misclassification rate criteria. However, this function was missing something important. The terms summing the number of samples misclassified above and below the split point should have been normalized.
Specifically, the term summing the number of samples misclassified above the split should have been divided by the total number of samples above the split and the term summing the number of samples misclassified below the split should have been divided by the total number of samples below the split.
Homework 2 Corrections and Clarifications: The original homework assignment stated there was a third optional question.
This was incorrect. There are only two required and no optional questions. When using the MAP estimate for question 2.
Ensure that you are not adding Beta - 1 to your word counts, since that may result in negative probabilities. Also, we are hallucinating each word appearing Beta times in the entirety of the training set, not Beta times per document.
Homework 3 Corrections and Clarifications: Question 3. Question 2 d : Please replace the current hint by this hint: "Your expression can involve integrals, and if it does you need not solve them. However, there is a much simpler answer that does not involve integrals so look for that!
Homework 5 Corrections and Clarifications: Question 3. However, these vectors should have been indexed by i. The homework file has been updated.
Previous material. Format - Submitted homeworks may be either typed or handwritten. However, for ease of grading, please submit answers to the individual questions that make up each homework assignment on separate pieces of paper.
When turning in code, please both print and attach a copy of your code to your homework and submit your code through the course blackboard website. Note - We might reuse problem set questions from previous years, covered by papers and webpages, we expect the students not to copy, refer to, or look at the solutions in preparing their answers.Thankyou for your solutions : I have 2 questions : 1 I see that the sizes of test set and validation set are 21X1 each while that of training set is only 12X1, why is the training set's size smaller that the test and validation set?
These codes are not working for me. Is there any other code from other website which is working for you? If that's the case, Please let me know I will recheck my codes. Otherwise, You must be doing small mistake from your end. Either way please let me know. Your code has been working for me. Thanks for your help. Recent Posts. Don't just copy paste the code for the sake of completion. Even if you copy the code, make sure you understand the code first.
Feel free to ask doubts in the comment section. I will try my best to solve it. If you find this helpful by any mean like, comment and share the post. This is the simplest way to encourage me to keep doing such work.
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