Lecture 2: Permutations and Combinations
Lecture 3: Axioms of Probability
Observance of Martin Luther King Day. No lecture.
Lecture 4: Conditional Probability and Bayes Rule
Section 1: Combinatorics and Probability
Lecture 6: Random Variables and Expectation
Lecture 7: Variance, Bernoulli, Binomial
Section 2: Random Variables and Expectation
Lecture 8: Poisson and Approximations
Lecture 9: Continuous Random Variables
PSet 2 In
Lecture 10: The Normal Distribution
Section 3: Discrete and Continuous Random Variables
Lecture 11: Joint Distributions
Lecture 12: Independent Random Variables
Lecture 13: Joint RV Statistics
Section 4: Normal Distributions and Joint Distributions
Lecture 14: Conditional Expectation
PSet 3 In,
PSet 4
Out,
CS109 Challenge
Out
Ross: Ch 7.1-7.2,
Piech: No assigned reading
Lecture 15: General Inference
Lecture 16: Continuous Joint Distributions
Section 5: Conditional Expectation
Lecture 17: Continuous Joint Distributions II
Ross: Ch 7.3-7.4,
Piech: no assigned reading
Observance of Presidents Day. No lecture.
PSet 4 In
Lecture 18: Central Limit Theorem
Section 6: Continuous Joint Distributions, Central Limit Theorem
Lecture 19: Sampling and Bootstrapping
Lecture 20: Parameters and MLE
Lecture 21: Beta
Ross: Ch 5.6.1-5.6.4, 7.5-7.6,
Piech:
Beta
Section 7: Boostrapping and MLE
Lecture 22: Maximum a Posteriori
Lecture 24: Linear Regression, Gradient Ascent
Ross: No assigned reading.,
Piech: No assigned reading.
Section 8: Parameter Estimation, Beta, and Naive Bayes
Lecture 25: Logistic Regression
Lecture 26: Deep Learning
Ross: No assigned reading.,
Piech: No assigned reading.
Lecture 27: Ethics in Probability and AI
Ross: No assigned reading.,
Piech: No assigned reading.
Section 9: Final Exam Review
Note that all lectures and assignment deadlines are subject to change.
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