Lecture Notes
Intro to Probability
- Basic Probability & Set Theory: Lecture 1 , Annotated Lecture 1
- Combinatorics: Lecture 2 , Annotated Lecture 2
- Conditional Probability & Independence: Lecture 3 , Annotated Lecture 3
- Bayes Rule: Lecture 4 , Annotated Lecture 4
Discrete Distributions
- Random Variables & Distributions: Lecture 5 , Annotated Lecture 5
- Expectation & Variance: Lecture 6 , Annotated Lecture 6
- Common Discrete Distributions
- Bernoulli & Binomial Distributions: Lecture 7 , Annotated Lecture 7
- Geometric & Poisson Distributions: Lecture 8 , Annotated Lecture 8
- Summary: Discrete Distributions
- Joint PMF: Lecture 9 , Annotated Lecture 9
Continuous Distributions
- Continuous Random Variables: Lecture 11 , Annotated Lecture 11
- Common Continuous Distributions
- Uniform Distribution: Lecture 12 , Annotated Lecture 12
- Exponential Distribution: Lecture 13 , Annotated Lecture 13
- Gamma Distribution: Lecture 14 , Annotated Lecture 14
- Normal Distribution: Lecture 15 , Annotated Lecture 15
- Summary: Continuous Distributions
- Central Limit Theorem: Lecture 16 , Annotated Lecture 16
- Interactive Practice: Google Colab Notebook: CLT
Stochastic Process
- Stochastic Process & Markov Chain: Lecture 17 , Annotated Lecture 17 , Additional Example , Additional Example Solution
- Steady-State Markov Chain: Lecture 18 , Annotated Lecture 18
Intro to Statistics
- Intro to Statistics: Lecture 19 , Annotated Lecture 19
- Descriptive Statistics
- Numerical Summaries: Lecture 20 , Annotated Lecture 20
- Graphical Summaries: Lecture 21 , Annotated Lecture 21
- Estimation
- Parameter Estimation: Lecture 22 , Annotated Lecture 22
- Method of Moments & Maximum Likelihood: Lecture 23 , Annotated Lecture 23 , Additional Example
- Confidence Intervals
- Confidence Intervals: Lecture 24 , Annotated Lecture 24
- Confidence Intervals (difference in groups): Lecture 25 , Annotated Lecture 25
- Hypothesis Testing: Lecture 26:Part 1 , Part 2 , Annotated Lecture 26
- Regression: Lecture 27 , Annotated Lecture 27