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Description

Probability is one of the most important ideas in human knowledge. This is a crash course to introduce the concept of probability formally; and exhibit its applications in computer science, combinatorics, and algorithms. The course will be different from a typical mathematics course in the coverage and focus of examples. After finishing this course a student will have a good understanding of both theory and practice of probability in diverse areas.INTENDED AUDIENCE : Computer Science & Engineering, Mathematics, Electronics, Physics, Statistics, & similar disciplines.PREREQUISITES : NilINDUSTRY SUPPORT : Machine Learning, Data Streaming, Discrete Optimization, Cryptography, Coding theory, Computer Algebra, Cyber

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Syllabus

Week 1: Introductory examples. Probability for finite space. Week 2: Sigma algebra. Conditional probability Week 3: Expectation. Famous random variables. Week 4: Concentration inequalities. Boosting by Chernoff. Week 5: Stochastic process. Week 6: Stationary distribution examples. Week 7: Probabilistic method examples. Week 8: Streaming algorithms.

  • Type
    Online Courses
  • Provider
    Swayam

Probability is one of the most important ideas in human knowledge. This is a crash course to introduce the concept of probability formally; and exhibit its applications in computer science, combinatorics, and algorithms. The course will be different from a typical mathematics course in the coverage and focus of examples. After finishing this course a student will have a good understanding of both theory and practice of probability in diverse areas.INTENDED AUDIENCE : Computer Science & Engineering, Mathematics, Electronics, Physics, Statistics, & similar disciplines.PREREQUISITES : NilINDUSTRY SUPPORT : Machine Learning, Data Streaming, Discrete Optimization, Cryptography, Coding theory, Computer Algebra, Cyber

Week 1: Introductory examples. Probability for finite space. Week 2: Sigma algebra. Conditional probability Week 3: Expectation. Famous random variables. Week 4: Concentration inequalities. Boosting by Chernoff. Week 5: Stochastic process. Week 6: Stationary distribution examples. Week 7: Probabilistic method examples. Week 8: Streaming algorithms.

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