Computational Probability and Inference

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8/10 stars
based on  20 reviews
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Cost FREE
Start Date On demand

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Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
22042 reviews

Course Description

Probability and inference are used everywhere. For example, they help us figure out which of your emails are spam, what results to show you when you search on Google, how a self-driving car should navigate its environment, or even how a computer can beat the best Jeopardy and Go players! What do all of these examples have in common? They are all situations in which a computer program can carry out inferences in the face of uncertainty at a speed and accuracy that far exceed what we could do in our heads or on a piece of paper.

In this data analysis and computer programming course, you will learn the principles of probability and inference. We will put these mathematical concepts to work in code that solves problems people care about. You will learn about different data structures for storing probability distributions, such as probabilistic graphical models, and build efficient algorithms for reasoning with these data str...

Probability and inference are used everywhere. For example, they help us figure out which of your emails are spam, what results to show you when you search on Google, how a self-driving car should navigate its environment, or even how a computer can beat the best Jeopardy and Go players! What do all of these examples have in common? They are all situations in which a computer program can carry out inferences in the face of uncertainty at a speed and accuracy that far exceed what we could do in our heads or on a piece of paper.

In this data analysis and computer programming course, you will learn the principles of probability and inference. We will put these mathematical concepts to work in code that solves problems people care about. You will learn about different data structures for storing probability distributions, such as probabilistic graphical models, and build efficient algorithms for reasoning with these data structures.

By the end of this course, you will know how to model real-world problems with probability, and how to use the resulting models for inference.

You don’t need to have prior experience in either probability or inference, but you should be comfortable with basic Python programming and calculus.

 

“I love that you can do so much with the material, from programming a robot to move in an unfamiliar environment, to segmenting foreground/background of an image, to classifying tweets on Twitter—all homework examples taken from the class!” – Previous Student in the residential version of this new online course.

Reviews 8/10 stars
20 Reviews for Computational Probability and Inference

Ratings details

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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student

6/10 starsTaking Now
2 years, 5 months ago
This course takes a great stab trying to teach probability while pairing it with an understanding of how to use computation to make it all better. That said- they may have oversold how much of an intro course it was. They are clear about the python experience, but a really great probability/stats course should probably be in your pocket as well. I found the advice of the other poster, completing MITx: 6.041x Introduction to Probability , to be very useful. That course I agree is a "master-piece". I've paused my progress in this course to go complete 6.041x first, and then intend to return here so that I maximize my understanding. Still-great course, and very appreciative for the effort in making this knowledge open!
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Borys Zibrov profile image
Borys Zibrov profile image
10/10 starsCompleted
  • 9 reviews
  • 8 completed
1 year, 11 months ago
Well, I really love courses that are challenging, that make you think, that leave you with a feeling you know little indeed, that give you this sense of victory after periods of frustration. But of course, there are people that hate that. So, I give 5/5, some will give 1. But well, I quickly forget stuff that I learn, and if I don't have to think much about the problem it will be gone in a week, in a month, not with the problems in this course. So, course is roughly divided into 3 parts: probability intro, learning HMMs and inference. Intro to probability was very steep and left me with the sensation I have to take that other great MIT course on probability mentioned here in reviews (6.041x). I liked the instructor and the videos, he was really passionate about the subject and active in the forums (I was lucky to catch the start of the session) which made it so much more fun to learn. Exercises were challenging and there was quit... Well, I really love courses that are challenging, that make you think, that leave you with a feeling you know little indeed, that give you this sense of victory after periods of frustration. But of course, there are people that hate that. So, I give 5/5, some will give 1. But well, I quickly forget stuff that I learn, and if I don't have to think much about the problem it will be gone in a week, in a month, not with the problems in this course. So, course is roughly divided into 3 parts: probability intro, learning HMMs and inference. Intro to probability was very steep and left me with the sensation I have to take that other great MIT course on probability mentioned here in reviews (6.041x). I liked the instructor and the videos, he was really passionate about the subject and active in the forums (I was lucky to catch the start of the session) which made it so much more fun to learn. Exercises were challenging and there was quite a great deal of code to write so often I had to stay up until late at night, plus there were also quizzes where one had to calculate by hand. I usually do not read mooc forums as it's often easy to complete exercises myself but during this course I used forums a lot because very often I had no clue what should I do in the exercises or in what direction should I proceed (and seems like I was not alone). Yes, it was often frustrating but I learnt and understood much more in the end. Also, there was an interesting time series analysis project in the end of the course but it was optional and I didn't have much time as it was holiday season so I just made sure forward-backward algorithm doesn't work with that data and didn't press it through the end, but it was an interesting challenge as well. To sum it up, I'm very grateful to MIT, instructors, and all the course participants for this wonderful opportunity to learn.
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Student

8/10 starsCompleted
2 years ago
The reviews for this course are a bit polarized and I think the lower ratings are largely a result of a poor communication of clear expectations of minimum prerequisites and what this course aims to teach. While there were some teething pains with this 1st offering of the course, I have to say that I found the material extremely interesting. It is true that the course lacks more formal/theoretical aspects in many areas, but this is only an issue if someone has not had any previous exposure to probability theory such as the outstanding 6.041X course on edX. I personally feel that the 6.041x should be made a prerequisite for this class as it would make tackling a lot of the content here a lot easier and you would also gain a lot more from the course. It would also then do away with the lack of more formal/theoretical explanations as being a negative. Still, the course was outstanding and I loved getting a broad exposure to numerous... The reviews for this course are a bit polarized and I think the lower ratings are largely a result of a poor communication of clear expectations of minimum prerequisites and what this course aims to teach. While there were some teething pains with this 1st offering of the course, I have to say that I found the material extremely interesting. It is true that the course lacks more formal/theoretical aspects in many areas, but this is only an issue if someone has not had any previous exposure to probability theory such as the outstanding 6.041X course on edX. I personally feel that the 6.041x should be made a prerequisite for this class as it would make tackling a lot of the content here a lot easier and you would also gain a lot more from the course. It would also then do away with the lack of more formal/theoretical explanations as being a negative. Still, the course was outstanding and I loved getting a broad exposure to numerous concepts. The programming challenges were also quite difficult at times and made you really go back to the basics to ensure that you truly understand the material taught in the course.
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mark m profile image
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mark m

6/10 starsDropped
1 year, 10 months ago
Challenging. In my view most people will struggle if they don't have some prior knowledge of probability notation (as well as the ability to program in python). With those pre-requisites in hand, I think this course could be rewarding particularly with the focus expanded to the computational elements of probability, which is in itself notationally quite dense.
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Sandeep Paul profile image
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Sandeep Paul

4/10 starsTaking Now
2 years, 5 months ago
I really hate to give any course a low score or to be critical just because of the fact one learns some thing from the course and the fact that instructors have put in some good effort in it no matter how bad the outcome, but this is a very complicated and unorganized course, many difficult things are taught in short bursts of lectures and very complicated concepts are not taught in a clear manner.
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Ming Fei Lau profile image
Ming Fei Lau profile image

Ming Fei Lau

10/10 starsTaking Now
2 years, 6 months ago
This course is excellent well-designed. You can simultaneously practice your python coding skills here if you have no prior python experience. On its main page it says the expected effort is 4-6 hours per week but to be frank, I was stuck at the mini-project for almost 20 hours. Fortunately many kind learners in the discussion forum provided very helpful hints to get my problem finally solved. I have a weak math and probability background so the actual effort that I have put in this course is really out of my expectation. As edX provides me such a good opportunity to get the best practice, words can't express my gratitude towards all the MITx staffs and instructors..
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Henry Harya profile image
Henry Harya profile image
8/10 starsDropped
  • 6 reviews
  • 5 completed
2 years, 4 months ago
The material is thought provoking and challenging in a good way. One should have a firm grounding in probability before taking this course though. Unfortunately this was the first time this course was offered and they were having serious teething problems, like issues with the grader, unclear instructions, trick questions, and too few practice problems and coding exercises. I have very little time as it is, and I spent too little time learning and too much time being frustrated, so I quit halfway through. I would be interested in taking this course again if they offer it, in the hopes that they improve and refine the course for the edX platform.
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student

1/10 starsTaking Now
2 years, 6 months ago
One of the good thing about online courses is the fact that sooner or later, you can compare them and realize which is better than the other one. Moreover, sooner or later, you realize that a good and prepared instructor is like a good storyteller. As of this writing, we are in week 4 of this course mostly covering probability. But based on this partial experience, I can tell you that this course does not have a good story line! Videos are short with sudden jumps in topics and instructors. It is not clear why one needs python to learn probability. If you want to do yourself a favor and learn probability properly and give the instructors of this course time to brush up their content, take another course which is also from MIT and available on edX: Introduction to Probability - The Science of Uncertainty by a Master John Tsitsiklis. The difference is like watching a class B movie versus a masterpiece.
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Student

8/10 starsCompleted
2 years, 4 months ago
The course covers graduate level coursework with few advance topics. The course assignments, project work are challenging, more mathematically oriented and requires a certain amount of dedication each week. Sometimes i struggled to get the solution for hours. The prior experience with python programming help a great. I was not able to complete all the homework and assignments but still learned a lot. Enjoyed the course and kind support from Course staff and TA's. Thanks Edx, MITx and Dr. Chen for offering such a wonderful course. Looking for next offering of this course.
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Andrey Spitsyn profile image
Andrey Spitsyn profile image

Andrey Spitsyn

6/10 starsTaking Now
2 years, 4 months ago
The creators of the course, unfortunately, not been able to link theory with practice. Programming exersizes very wealky related with theory and after cuple of week of theory and quizzes you're not receive programming practice which explain how to apply it to world related issues.
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Student

10/10 stars
2 years, 4 months ago
When is the next intake of this course? I im enrolled in this course but I cannot complet it due to some factors. It will be great if the next intake is in December or in January.
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Rajat Thomas profile image
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Rajat Thomas

10/10 starsCompleted
2 years, 4 months ago
Let us be clear. Don't do this course as a first course in probability. Sufficient knowledge of basic probability will let you focus on the topic. It is NOT easy. But, if you get through this, you can pretty much get a grasp of some of the cutting edge things being done in graphical models and inference. Prepare to spend a LOT of time understanding and doing the exercises. It is rewarding!
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Yaman Qoudiematy profile image
Yaman Qoudiematy profile image

Yaman Qoudiematy

10/10 starsTaking Now
2 years, 5 months ago
Great Course with Great Staff. the content is very well sequenced. The projects let you solidify your understanding, and apply the concepts you've learned in a very realistic manner. assignments combines theory and practice along with gaining sight of the computational complexity.
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Jiang Xiaokun profile image
Jiang Xiaokun profile image

Jiang Xiaokun

9/10 starsTaking Now
2 years, 5 months ago
Nice course about probability and graph model. mini-project 1 is funny. Thank George H. Chen and 6.008.1x team.
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student

9/10 starsTaking Now
2 years, 6 months ago
This course is a bit rough around the edges, but it's been a great 4 weeks with the difficulty ramping up in week 4. In general, it seems to be an interesting approach to exploring and teaching a number of concepts (some that are quite tricky) and seems to succeed most of the time. The only complaint is the lack of participation from staff on the forums. That being said, there are a great set of community TAs and lots of active discussion on the forums that makes it a lot easier to ask questions and look for hints when stuck on problems.
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Leonardo dos Santos Pinheiro profile image
Leonardo dos Santos Pinheiro profile image

Leonardo dos Santos Pinheiro

10/10 starsTaking Now
2 years, 6 months ago
This is an awesome course. It uses an interesting approach to teach probability that is way more useful for computational modeling than traditional approaches. It also has a nice emphasis on bayesian and graphical models that is hard to find in other online courses. I highly recommend it.
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An Iranian profile image
An Iranian profile image

An Iranian

1/10 starsDropped
2 years, 7 months ago
Iran has been gone over sanctions, why Iranian still cannot take this course or any other edx courses?
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Florian Bayer profile image
Florian Bayer profile image

Florian Bayer

10/10 starsTaking Now
2 years, 6 months ago
I'm very content with the course so far. I took several MOOCs already on different platforms and this one belongs to the best ones I've ever participated in (like many other MITx Courses). Looking so much forward to the next chapter that is starting next Monday. I totally recommend this course to everyone who wants a broad and comprehensive introduction into statistic and statistical computation.
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10/10 starsTaking Now
  • 10 reviews
  • 1 completed
2 years, 7 months ago
Amazing! This is as close to a college experience as possible, on edX. Thank you thank you! Tons of materials to learn and practice probability, yes, you'll get to code and do projects too!
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Manjeet Chandra profile image
Manjeet Chandra profile image

Manjeet Chandra

10/10 starsTaking Now
2 years, 8 months ago
I am really excited for this course. Introduction to the subject as described is so much closely related to my area of interest. My inclination is towards Machine Learning and I am pretty much sure that this course will help me a lot. Thanks MITx and edX.
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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.