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S# Lecture Course Institute Instructor Discipline
3451
19. Weak Law of Large Numbers (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3452
Lecture 3: Bit Hacks (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Julian Shun Applied Sciences
3453
2. Conditioning and Bayes' Rule (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3454
Lecture 4: Assembly Language & Computer Architecture (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Charles Leiserson Applied Sciences
3455
20. Central Limit Theorem (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3456
Lecture 5: C to Assembly (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Tao B. Schardl Applied Sciences
3457
21. Bayesian Statistical Inference I (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3458
Lecture 6: Multicore Programming (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Julian Shun Applied Sciences
3459
22. Bayesian Statistical Inference II (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3460
Lecture 7: Races and Parallelism (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Charles Leiserson Applied Sciences
3461
23. Classical Statistical Inference I (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3462
Lecture 8: Analysis of Multithreaded Algorithms (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Charles Leiserson Applied Sciences
3463
24. Classical Inference II (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3464
Lecture 9: What Compilers Can and Cannot Do (M-I-T)
Performance Engineering of Software Systems (Fall 2018) (M-I-T) MIT Prof. Dr. Tao B. Schardl Applied Sciences
3465
25. Classical Inference III (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3466
3. Independence (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3467
4. Counting (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3468
5. Discrete Random Variables I (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3469
A Chess Tournament Problem (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3470
6. Discrete Random Variables II (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3471
A Coin Tossing Puzzle (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3472
7. Discrete Random Variables III (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3473
A Coin with Random Bias (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3474
8. Continuous Random Variables (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences
3475
A Derived Distribution Example (M-I-T)
Probabilistic System Analysis And Applied Probability (Fall 2013) (M-I-T) MIT Prof. Dr. John Tsitsiklis Applied Sciences