Introduction and Proofs (MIT 6.042J, Lecture 1)
44:09Mathematical Induction (MIT 6.042J, Lecture 2)
1:19:25Strong Induction (MIT 6.042J, Lecture 3)
1:22:00Number Theory I (MIT 6.042J, Lecture 4)
1:20:24Number Theory II (MIT 6.042J, Lecture 5)
1:18:45Graph Theory and Coloring (MIT 6.042J Mathematics for Computer Science, Lecture 6)
1:22:51Matching Problems (MIT 6.042J Mathematics for Computer Science, Lecture 7)
1:22:40Graph Theory II: Minimum Spanning Trees (MIT 6.042J, Lecture 8)
1:23:26Lecture 9: Communication Networks (6.042J)
1:23:25Lecture 10: Graph Theory III (6.042J)
1:22:50Lecture 11: Relations, Partial Orders, and Scheduling (6.042J)
1:04:00Lecture 12: Sums (6.042J)
1:18:22Lecture 13: Sums and Asymptotics (6.042J)
1:23:40Divide and Conquer Recurrences (MIT 6.042J, Lecture 14)
1:22:46Lecture 15: Linear Recurrences (6.042J)
1:18:20Lecture 16: Counting Rules I (6.042J)
1:20:02Lecture 17: Counting Rules II (6.042J)
1:25:24Lecture 18: Probability Introduction (6.042J)
1:23:56Lecture 19: Conditional Probability (6.042J)
1:21:46Lecture 20: Independence (6.042J)
1:22:02Lecture 21: Random Variables (6.042J)
1:22:59Lecture 22: Expectation I (6.042J)
1:23:54Lecture 20: Independence (6.042J)
This session defines statistical independence between events, discussing its implications for probability calculations and introducing concepts like Bernoulli trials.
Lecture 20: Independence
Instructor: Tom Leighton
View the complete course: http://ocw.mit.edu/6-042JF10
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Lecture 19: Conditional Probability (6.042J)
Next · Lecture 21Lecture 21: Random Variables (6.042J)
Seminar notes & discussion
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