Introduction to Probability: Part 1 - The Fundamentals

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Introduction to Probability: Part 1 - The Fundamentals

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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.

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Course Description

The world is full of uncertainty: accidents, storms, unruly financial markets, and 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.

This is Part 1 of a 2-part sequence on the basic tools of probabilistic modeling. Part 1 introduces the general framework of probability models, multiple discrete or continuous random variables, expectations, conditional distributions, and various powerful tools of general applicability. Part 2 will then continue into further topics that include laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains).

The contents of the two parts of the course are essentially the same as those of the corresponding MIT class, which has been offered and continuously refined over...

The world is full of uncertainty: accidents, storms, unruly financial markets, and 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.

This is Part 1 of a 2-part sequence on the basic tools of probabilistic modeling. Part 1 introduces the general framework of probability models, multiple discrete or continuous random variables, expectations, conditional distributions, and various powerful tools of general applicability. Part 2 will then continue into further topics that include laws of large numbers, the main tools of Bayesian inference methods, and an introduction to random processes (Poisson processes and Markov chains).

The contents of the two parts of the course are essentially the same as those of the corresponding MIT class, which has been offered and continuously refined over more than 50 years. It is a challenging class, but will enable you to apply the tools of probability theory to real-world applications or your research.

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.

Photo by User: Pablo Ruiz Múzquiz on Flickr. (CC BY-NC-SA 2.0)

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Reviews 9/10 stars
3 Reviews for Introduction to Probability: Part 1 - The Fundamentals

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Robinson Odhiambo profile image
Robinson Odhiambo profile image

Robinson Odhiambo

10/10 starsDropped
1 year, 3 months ago
The concepts in the course were clearly presented. With worked examples. I really learnt a lot. I didn't finish the course because I used it to brush up on the subject
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Antonello Lobianco profile image
Antonello Lobianco profile image

Antonello Lobianco

10/10 starsTaking Now
1 year, 5 months ago
Don't ask me why, probability has never popped up within the subjects I has been taught (you can't choose in Italy) and now I really miss it in my career as Forest Economist. I think this course is close to the perfection: content is super, challenging and rigorous, but with every question/doubt I come up in my mind straight ahead answered by the lecturer. How he did know I had that precise question ? The form is also perfect, with a video of the lecturer to introduce the topic and then the focus goes directly on the slides, with the lesson divided in short segments plus quizzes to reinforce learning. I can see there has been lot of work on the "form" (but not on the content that - I tested it - is exactly the same) from the first version of the course published on the OCW, where the lectures there were "just" registrations of the actual class lessons. This reinforce a more general tough, that when moving an activity online, to tak... Don't ask me why, probability has never popped up within the subjects I has been taught (you can't choose in Italy) and now I really miss it in my career as Forest Economist. I think this course is close to the perfection: content is super, challenging and rigorous, but with every question/doubt I come up in my mind straight ahead answered by the lecturer. How he did know I had that precise question ? The form is also perfect, with a video of the lecturer to introduce the topic and then the focus goes directly on the slides, with the lesson divided in short segments plus quizzes to reinforce learning. I can see there has been lot of work on the "form" (but not on the content that - I tested it - is exactly the same) from the first version of the course published on the OCW, where the lectures there were "just" registrations of the actual class lessons. This reinforce a more general tough, that when moving an activity online, to take advantage of the new media, you have to adapt your processes to the new media and not just copy what you were used to do offline. If I only miss something, on the side of the content is a bit of linear algebra (matrix calculus).. why I understand it is not required for the course, sometimes it comes very natural, like when speaking of independence between random variables.. On the form instead (but this is a limit of edX, not the course itself) the forum software really sucks, and I guess this is why it is very little used. It should deserve a deep redesign ! Overall a great course that opened my mind to a full new world !
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Mohammad Ezzat profile image
Mohammad Ezzat profile image

Mohammad Ezzat

9/10 starsCompleted
1 year, 7 months ago
As a statistics student this is the greatest course I have ever seen .. good idea to separate contents into two individual courses. Waiting to see the same crew in a completing course in further topics like modeling and hypothesis testing or even a course about using SPSS in data analysis >>> I have only one annoying question: after this course (assuming that I'm perfect ) what to learn after that and how to earn money with such science??? wish you are good dear crew :D
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