Hello everyone!

New content has been posted! To see the latest videos, please go to https://class.coursera.org/ml-004/lecture/index.

We've uploaded Week 9 content. In these videos, you will learn about anomaly detection, which is widely used in fraud detection (e.g. "has this credit card been stolen?"). You'll also learn about recommender systems, which are used by companies like Amazon, Netflix and Apple to recommend products to their users. In the corresponding programming exercise, you will get to implement both an anomaly detection system and a recommender system. The review questions and programming exercise for Week 9 are due on Sunday, December 29 at 11:59 PM Pacific Time.

As always, if you get stuck on the review questions and programming exercise, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

We've also uploaded content for the tenth and final week! In these videos, you will learn about machine learning with lots of data. With the amount data that many websites/companies are gathering today, knowing how to handle "big data" is one of the most sought after skills in Silicon Valley. In addition, we will also walk you through a complex, end-to-end application of machine learning, to the application of Photo OCR. There will not be a programming exercise for the Week 10 content, but there are still review questions. The questions for Week 10 are due on Sunday, January 5 at 11:59 PM Pacific Time.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 23 Dec 2013 9:01 AM CET

Hello everyone!

New content has been posted! To see the latest videos, please go to https://class.coursera.org/ml-004/lecture/index.

This week, you will be learning about unsupervised learning. While supervised learning algorithms need labeled examples (x,y), unsupervised learning algorithms need only the input (x). You will learn about clustering--which is used for market segmentation, text summarization, among many other applications--and PCA, which is used to speed up learning algorithms, and is sometimes incredibly useful for visualizing and helping you to understand your data. The review questions and programming exercise for Week 8 are due on Sunday, December 22 at 11:59 PM Pacific Time.

As always, if you get stuck on the review questions and programming exercise, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

We've also uploaded Week 9 content. In these videos, you will learn about anomaly detection, which is widely used in fraud detection (e.g. "has this credit card been stolen?"). You'll also learn about recommender systems, which are used by companies like Amazon, Netflix and Apple to recommend products to their users. In the corresponding programming exercise, you will get to implement both an anomaly detection system and a recommender system. The review questions and programming exercise for Week 9 are due on Sunday, December 29 at 11:59 PM Pacific Time.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 16 Dec 2013 9:01 AM CET

Dear Machine Learning Students,

A few of you pointed out that the hard deadlines for the exercises on Linear Regression and Logistic Regression were not consistent with those for the rest of the course. We've corrected this, so that all hard deadlines are now set to January 12, 2014. If you previously submitted either of these assignments late (after the incorrect hard deadline) you should now see that your score has been updated to reflect the adjusted deadline.

Thanks for your patience, and for helping us correct this problem!

Best,

The Coursera Team

Wed 11 Dec 2013 11:59 PM CET

Hello everyone!

New content has been posted! To see the latest videos, please go to https://class.coursera.org/ml-004/lecture/index.

This week, you will be learning about the support vector machine (SVM) algorithm. SVMs are considered by many to be the most powerful "black box" learning algorithm, and by posing a cleverly-chosen optimization objective, one of the most widely used learning algorithms today. The review questions and programming exercise for Week 7 are due Sunday, December 15 at 11:59 PM Pacific Time.

As always, if you get stuck on the review questions and programming exercise, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

We have also uploaded Week 8 content, which is about a brand new topic: Unsupervised learning. Whereas supervised learning algorithms needs labeled examples (x,y), unsupervised learning algorithms need only the input (x). You will learn about clustering--which is used for market segmentation, text summarization, and other applications--and PCA, which used to speed up learning algorithms, and is sometimes incredibly useful for visualizing and helping you to understand your data. The review questions and programming exercise for Week 8 are due on Sunday, December 22 at 11:59 PM Pacific Time.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 9 Dec 2013 9:01 AM CET

Hello everyone!

New content has been posted! To see the latest videos, please go to https://class.coursera.org/ml-004/lecture/index.

In Week 6, you will be learning about systematically improving your learning algorithm. The videos for this week will teach you how to tell when a learning algorithm is doing poorly, and describe the "best practices" for how to "debug" your learning algorithm and go about improving its performance. When you're applying machine learning to real problems, a solid grasp of this week's content will easily save you a large amount of work. The review questions and programming exercise for Week 6 are due on Sunday, December 8 at 11:59 PM Pacific Time.

As always, if you get stuck on the review questions and programming exercise, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

We've also posted the Week 7 content! These videos discuss the support vector machine (SVM), which is a learning algorithm that optimizes a different objective from ones that you have learned about so far. Using kernels, it is also very easy to modify SVMs to learn non-linear decision boundaries. Most people consider the SVM to be the most powerful "black box" learning algorithm. The review questions and programming exercise for Week 7 are due Sunday, December 15 at 11:59 PM Pacific Time.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 2 Dec 2013 9:01 AM CET

Hi all,

New content has been posted! To see the latest videos, please go to https://class.coursera.org/ml-004/lecture/index.

In Week 5, you will be learning how to train Neural Networks. The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and this week's videos explain the "backprogagation" algorithm for training these models. In this week's programming exercise (due Sunday, December 1 at 11:59 PM Pacific Time), you'll also get to implement this algorithm and see it work for yourself. The review questions on "Neural Networks: Learning" are also due at the same time.

The Neural Network programming exercise will be one of the more challenging ones of this class. So please start early and do leave extra time to get it done, and I hope you'll stick with it until you get it to work! Also, as always, in case you get stuck or have a question, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

In addition, we've also posted online the Week 6 videos (for next week). These videos which teach you how to tell when a learning algorithm is doing poorly, and describes the "best practices" for how to "debug" your learning algorithm and go about improving its performance. When you're applying machine learning to real problems, a solid grasp of the Week 6 content will easily save you months of work. The review questions and programming exercise for the Week 6 content is due on Sunday, December 8 at 11:59 PM Pacific Time.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 25 Nov 2013 9:01 AM CET

Hello all!

I hope everyone has been enjoying the course and learning a lot! The video lectures, programming exercises, and review questions for Week 5 are now available.

The due date for the third programming exercise is quickly approaching. It is due this Sunday, November 24 at 11:59 PM Pacific Time. Remember that you can use our course wiki to share useful advice with your classmates about the course content, review quizzes, and programming exercises. Also, in case you get stuck or have a question, you should post on the Discussion Forum to ask for help. (And if you finish early, I hope you'll go there to help your fellow classmates as well.)

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 18 Nov 2013 9:01 AM CET

Hello all!

I hope everyone has been enjoying the course and learning a lot! The video lectures, programming exercises, and review questions for Week 4 are now available.

The due date for the second programming exercise is quickly approaching. It is due this Sunday, November 17 at 11:59 PM Pacific Time. Remember that you can use our course wiki to share useful advice with your classmates about the course content, review quizzes, and programming exercises.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 11 Nov 2013 9:01 AM CET

Dear students,

We apologize that there was an error in the submission of your Week 2 Programming Assignment file. If you downloaded the homework zip file before Friday, November 8 then you will see an error when you try to submit it.  We have now fixed the submit.m and submitWeb.m files and correct zip files should be named for each week in the format "mlclass-ex*-004.zip". To submit properly, we suggest that you re-download the homework zip file from: https://class.coursera.org/ml-004/assignment/index.

This has solved the problem for other students. If you continue to experience problems submitting the homework files, please report a technical issue here: https://class.coursera.org/ml-004/forum/list?forum_id=10001

We have also extended the submission deadline for this assignment by 24 hours so that you have time to submit your assignment properly. Thank you for your hard work, and we'll make sure to update the next weeks' homework zip files as well.

- Coursera Staff.

Mon 11 Nov 2013 8:56 AM CET

I hope you’ve been enjoying the course so far and have successfully submitted the first assignment. If you find that you have a lot of questions and want some extra help with the material, you may want to sign up for the tutoring sessions that begin today.

You can sign up for tutoring and find out more here: Coursera Machine Learning Tutoring Program.

Your tutors will be participants who have taken past sessions of this Machine Learning course and scored at the very top of the class based on Coursera assessments. Most tutors scored 100% on every programming assignment in the course and also were active in the discussion forums where they helped other participants last time the course ran. This is a peer-to-peer tutoring program, so please note that the tutors are neither provided by nor affiliated with Stanford University and have been selected solely by Coursera.

You don’t need to participate in tutoring to take the course and it won’t affect your grade if you don’t participate, but this will be a valuable extra resource for students who want more help. If you want extra help with the materials, I hope you’ll take advantage of it.

Remember that the second assignment for the course is due on November 10th. Good luck!

Andrew

Tue 5 Nov 2013 6:05 PM CET

Hello all!

I hope everyone has been enjoying the course and learning a lot! New material has just been posted, which you can see at https://class.coursera.org/ml-004/lecture/index.

The due date for the first programming exercise is quickly approaching. It is due this Sunday, November 10 at 11:59 PM Pacific Time. All submissions after this time will be penalized by 20%. If you miss the deadline, we encourage you to try to submit anyway, because you can still get some credit for it.

In addition, our course wiki is now open! The course wiki is a place where you can share useful advice with your classmates about the course content, review quizzes, and programming exercises.

Andrew Ng

Mon 4 Nov 2013 9:01 AM CET

Hello all!

I hope you have had a chance to explore the first two weeks of materials already. Today marks the beginning of our “official” Week 1. The due date for the first set of review quizzes is Sunday, November 3rd at 11:59 PM Pacific Time. Please remember to complete the Entrance Survey if you have not done so yet. Next week, we will also announce a one-time pilot program which may help some students do well in the course---stay tuned!

In Week 1, you’ll learn about the basics of machine learning, and what it can do for you. You’ll also learn about linear regression---a simple, but powerful and very widely used, learning algorithm. Through visualizations, you’ll get to understand what it is really doing, and gain intuition regarding how to make it work well.

To get started, please go to https://class.coursera.org/ml-004/class.

Andrew Ng

Mon 28 Oct 2013 8:01 AM CET

Welcome to Machine Learning! I'm excited to have you in the class and look forward to helping you become an expert in machine learning. I wanted to let you know that although we have posted the materials online as of today (the official start date of the course), the first assignment deadline will be November 3rd. Thus you can get started right away, you can still bring friends into the class anytime over the next two weeks, and they’ll still have ample time to complete assignment 1. You'll see these materials at class.coursera.org/ml-004/; you can also jump directly to Lecture #1.

After you finish watching the Week 1 lectures, there's also a set of Review Questions to help you check your understanding. You should be able to complete the review questions in a few minutes, so please attempt them before the deadline (November 3rd). You can attempt the review questions as many times as you like, and we will only use your highest score.

By the time you finish this class, you'll know how to apply the most advanced machine learning algorithms to such problems as anti-spam, image recognition, clustering, building recommender systems, and many other problems. You'll also know how to select the right algorithm for the right job, as well as become expert at "debugging" and figuring out how to improve a learning algorithm's performance.

In a few weeks, we will also announce a one-time pilot program which may help some students do well in the course---stay tuned!

This machine learning class is the class that started Coursera. I'm excited to be teaching it again, and look forward to seeing you in class!

Andrew Ng

Mon 14 Oct 2013 5:00 PM CEST