Introduction to Probability - The Science of Uncertainty

开始时间: 待定 持续时间: 16 weeks

所在平台: edX

课程类别: 数学

大学或机构: MITx

授课老师: ​Shomesh Chaudhuri John Tsitsiklis



课程评论: 3 个评论

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*Note - This is an Archived course*

The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem - proof" format, we develop the material in an intuitive -- but still rigorous and mathematically precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.

The course covers all of the basic probability concepts, including:

  • multiple discrete or continuous random variables, expectations, and conditional distributions
  • laws of large numbers
  • the main tools of Bayesian inference methods
  • an introduction to random processes (Poisson processes and Markov chains)

The contents of this course are essentially the same as those of the corresponding MIT class (Probabilistic Systems Analysis and Applied Probability) -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class, but it will enable you to apply the tools of probability theory to real-world applications or your research.

Can I still register after the start date?
You can register at any time, but you will not get credit for any assignments that are past due.

How long does this course last?
The course starts on Tuesday, February 3, 2015 and ends on the due date of the final exam, on Thursday, May 26, 2015.

What is the format of the class?
The course material is organized along units that are aligned with the chapters of the textbook. Each unit contains between one and three lecture sequences. Each lecture sequence consists of short video clips, interleaved with short problems to test your understanding. Each unit also contains a wealth of supplementary material, including videos that go through the solution of various problems.

What textbook do I need for the course?
None - there is no required textbook. The class follows closely the text Introduction to Probability, 2nd edition, by Bertsekas and Tsitsiklis, Athena Scientific, 2008; see the publisher's website or for more information. However, while this textbook is recommended, the materials provided by this course are self-contained.

Do I need to watch the lectures live?
Video lectures as well as worked problems will be available and you can watch these at your own convenience. Homework assignments and exams, however, will have due dates.

Will the text of the video clips be available?
Yes, we will provide transcripts of all clips (lectures, worked problems, etc.) that are synched to the videos.

How are grades assigned?
Grades (Pass or Not Pass) are based on a combination of scores on the weekly homework assignments (11 total), two midterm exams, and a final exam.

How much do I need to work for this class?
This is an ambitious class in that it covers a lot of material in substantial depth. Furthermore, the only way of mastering the subject is by actually solving on your own a fair number of problems. MIT students who take the corresponding residential class typically report an average of 11-12 hours spent each week, including lectures, recitations, readings, homework, and exams.

This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people enjoy watching the videos and working with the materials. Make sure to check for reruns of this course.


  • The basic structure and elements of probabilstic models
  • Random variables, their distributions, means, and variances
  • Probabilistic calculations
  • Inference methods
  • Laws of large numbers and their applications
  • Random processes



老爷不要啊啊啊 2014-07-27 15:13 0 票支持; 0 票反对

这门课由于开头5个Unit切题切的比较轻松, 所以前半段没有去论坛玩, Unit 6开始Pset不得不参考一下论坛了, 发现论坛其实是个很有趣的地方, 就"住"了下来, 不切题也去耍耍, 退休的职业Poker选手Mark, 数学博士毕业后做了一辈子心理医生的mmstoke, 程序员Bayram, 还有几个助教, 都有很好的交流, 有力量也会在论坛里答疑, 直到论坛关闭才离开. 之后加了2个群, 现在里面还有新帖子出现.


超級現實的超現實理想主義者 2014-06-09 11:07 0 票支持; 0 票反对





大吃一惊异 2014-06-07 14:03 0 票支持; 0 票反对

概率以前是我最喜欢的课,可惜离开学校几年之后就还给老师了。去Google面试的时候被问了很基础的题没有答好,所以用这门课复习了一下。课程内容是比较标准的本科生概率课,grading比较严格,作业难度我猜is what you'd expect from MIT。有些话题没有特别深入,比如markov chain只讲了离散的版本,不过抽了两周讲了estimation(似乎统计课才会涉及,以前没学过)。老师语速有点慢,开两倍速才不至于睡着。每讲一些概念就会有小quiz来进行sanity check很及时。每周的作业写完之后还是挺有成就感的。那本教材不错,当时课比较多,所以没有仔细看。

Deep Learning Specialization on Coursera


An introduction to probabilistic models, including random processes and the basic elements of statistical inference.


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