| Lecture # |
Topic |
Slides |
| 1 |
BST Permutations |
PDF
|
| 2 |
Combinations |
PDF
|
| 3 |
Introduction to Probability |
PDF
|
| 4 |
Conditional Probability and Bayes Theorem |
PDF
|
| 5 |
More on Conditional Probability and Independence |
PDF
|
| 6 |
Random Variables and Expectation |
PDF
|
| 7 |
Variance and Binomial Distribution |
PDF
|
| 8 |
Poisson and Other Discrete Distributions |
PDF
|
| 9 |
Continuous Distributions |
PDF
|
| 10 |
Normal and Exponential Distributions |
PDF
|
| 11 |
Joint Distributions |
PDF
|
| 12 |
Independent Random Variables |
PDF
|
| 13 |
Sums of Independent Random Variables and Conditional Distributions |
PDF
|
| 14 |
Properties of Expectation and QuickSort Analysis |
PDF
|
| 15 |
Covariance |
PDF
|
| 16 |
Correlation and Conditional Expectation |
PDF
|
| 17 |
Moment Generating Functions |
PDF
|
| 18 |
Helpful Inequalities and Bounds |
PDF
|
| 19 |
Law of Large Numbers and Central Limit Theorem |
PDF
|
| 20 |
Parameter Estimation and Method of Moments |
PDF
|
| 21 |
Maximum Likelihood Estimation |
PDF
|
| 22 |
Bayesian Estimation |
PDF
|
| 23 |
Machine Learning and Naive Bayes Classifier |
PDF
|
| 24 |
Logistic Regression |
PDF
|
| 25 |
Modeling Uncertainty and Utility |
PDF
|
| 26 |
Generating probabilities and Monte Carlo simulation |
PDF
|
| 27 |
Final Exam Review Examples |
PDF
|