6.041x: Introduction to Probability - The Science of Uncertainty

MITx

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

About this Course

*Note - This is an Archived course*

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 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.

Course Staff

  • John Tsitsiklis

    John Tsitsiklis is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering. He obtained his PhD from MIT and joined the faculty in 1984. His research focuses on the analysis and control of stochastic systems, including applications in various domains, from computer networks to finance. He has been teaching probability for over 15 years.

  • Patrick Jaillet

    Patrick Jaillet is a Professor of Electrical Engineering and Computer Science and Co-Director of the MIT Operations Research Center. He obtained his PhD in Operations Research at MIT. His research interests deal with optimization and decision making under uncertainty as applied to transportation and the internet economy. Professor Jaillet’s teaching includes subjects such as algorithms, optimization, and probability.

  • Dimitri Bertsekas

    Dimitri Bertsekas is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering. He obtained his PhD from MIT and joined the faculty in 1979. His research focuses on optimization theory and algorithms, with an emphasis on stochastic systems and their applications in various domains, such as data networks, transportation, and power systems. He has been teaching probability for over 15 years.

  • Qing He

    Qing He is a graduate student in the MIT Department of Electrical Engineering & Computer Science. Her research interests include inference, signal processing, and wireless communications -- all of which rely on the fundamental concepts taught in 6.041x. Qing has taken several probability classes at MIT, and has been a teaching assistant for this course for two semesters.

  • Jimmy Li

    Jimmy Li is a graduate student in MIT's Department of Electrical Engineering & Computer Science. His research focuses on applying the tools taught in this and related courses to problems in marketing. He took 6.041 as an undergraduate and has also been a TA for the course three times.

  • Jagdish Ramakrishnan

    Jagdish Ramakrishnan recently received his PhD at MIT’s Department of Electrical Engineering and Computer Science. His dissertation focused on optimizing the delivery of radiation therapy cancer treatments dynamically over time. His general research interests include systems modeling, optimization, and resource allocation. He was a teaching assistant for this course twice while at MIT.

  • Katie Szeto

    Katie Szeto is a member of the Business Operations team at Dropbox. She received her Bachelor and Master of Engineering degrees from MIT. Her Master’s thesis explored applications of probabilistic rank aggregation algorithms. Katie took 6.041 with Professor Tsitsiklis when she was a sophomore at MIT. Later, as a graduate student, she was a teaching assistant for the class.

  • Kuang Xu

    Kuang Xu is a graduate student in the Department of Electrical Engineering & Computer Science at MIT. His research focuses on the design and performance analysis of large-scale networks, such as data centers and the Internet, which involve a significant amount of uncertainties and randomness. Kuang took his first probability course in his junior year, and served as a teaching assistant for 6.041 in 2012.

会期:
  • 2014年2月04日, 15 星期
介绍:
  • 免费:
  • 收费:
  • 证书:
  • MOOC:
  • 视频讲座:
  • 音频讲座:
  • Email-课程:
  • 语言: 英语 Gb

反馈

目前这个课程还没有反馈。您想要留第一个反馈吗?

请注册, 为了写反馈

Show?id=n3eliycplgk&bids=695438
已经在列表:
18-06scf11 Mathematics
Calculus, Linear algebra, Functional and Complex Analysis and more
NVIDIA
还有这个题目的:
Ut.6.01x-banner-262x136_verified UT.6.01x: Embedded Systems - Shape The World
Build real-world embedded solutions using a bottom-up approach from simple to...
9-916as03 Probability and Causality in Human Cognition
Probability theory captures a number of essential characteristics of human cognition...
6.00x-listing-banner 6.00x: Introduction to Computer Science and Programming
6.00x is an Introduction to computer science as a tool to solve real-world analytical...
1-010f08 Uncertainty in Engineering
This course gives an introduction to probability and statistics, with emphasis...
Linear-circuits-icon600x340 Linear Circuits
Learn the analysis of circuits including resistors, capacitors, and inductors...
还有标题«数学与统计»:
D56de95b-18e8-4775-b347-34eddd72cfe2-5e0cbe8bc6a7.small 水力学 | Hydraulics
This Hydraulics course explores the science of hydraulics and focuses on hydrodynamic...
Bd426e10-8994-45bc-859f-c4259ff7a9e9-214b2fe72799.small 数据挖掘:理论与算法 | Data Mining: Theories and Algorithms for Tackling Big Data
Unraveling the mysteries of Data Mining and Big Data, this course is a must...
1ef08cfe-634e-468f-bfe9-55b6e8a7964d-052b875b6a6b.small Combinatorial Mathematics | 组合数学
Discover how to apply counting principles and combinatorics to solve problems...
Ad29f74c-7389-4608-b54f-570ef08b2f4e-1bed3b5f22d0.small How to Learn Math: For Students
How to Learn Math is a free self-paced class for learners of all levels of mathematics...
Fd92782d-f46f-401b-97d3-fc7bb0266c6f-0a0ff9ae9928.small Calculus 1C: Coordinate Systems & Infinite Series
Master the calculus of curves and coordinate systems—approximate functions...
还有edX:
5a631d1c-cb20-4cfc-9b49-1cc9c8fc981e-5949e438d0ed.small Introduction to Linux
Never learned Linux? Want a refresh? Develop a good working knowledge of Linux...
5efe7b5c-18f6-400c-9564-5ff7b520b2a8-a05b06b3aa6c.small 生活英语听说 | Conversational English Skills
Learn how to effectively communicate in English and improve your conversational...
3d5b33d7-448c-4057-a824-048ed13fd8f6-cbb1a987ce32.small 文物精品与文化中国:天文与医药 | Relics of Chinese History - Part 2: Astronomy and Medicine
Discover how Astronomy and Medicine impacted the history of China as we use...
77f224a0-c43a-4669-bb46-93648612d0a3-fff6ddd97136.small 文物精品与文化中国:文字与乐礼 | Relics of Chinese History - Part 3: Writing System, Rites and Music
Discover how writing systems and music played a major role in Chinese history...
0eec8a3f-2175-4800-b076-fba0e5349c65-f6048c117265.small 文物精品与文化中国:农业与制造业 | Relics in Chinese History - Part 1: Agriculture and Manufacturing
Discover manufacturing and agriculture’s role in the history of China as we...

© 2013-2019