CSC 21700: Probability and Statistics for Computer Science
The objective of this course is to help you learn to analyze data and use methods of statistical inference. Central to the course is the application of fundamental concepts covered in probability and decision making to the problem of drawing inferences from data on observed outcomes. Topics covered during the first part of the course will include statistical sampling and sampling distributions, point estimation and confidence intervals, hypothesis testing, correlations among variables, and regression. The second part of the course will focus on multivariate analysis, with special attention paid to the inferences that may drawn with respect to prediction and causality.
- Syllabus
- Assignments
- Projects
- Probability
- Events and their probabilities
- Rules of Probability
- Equally likely outcomes
- Combinatorics
- Conditional probability
- Independence
- Discrete Random Variables and Their Distributions
- Random Variables
- Discrete Distribution
- Joint and marginal distributions
- Independence
- Expectation and Variance
- Chebyshev's Inequality
- Discrete Distributions
- Continuous Distributions
- Probability Density
- Uniform Distribution
- Exponential Distribution
- Gamma Distribution
- Normal Distribution
- Central Limit Theorem
- Computer Simulations and Monte Carlo Methods
- Statistics
- Variable Types
- Population and Sample
- Sampling Designs
- Bad Sampling
- Simple Descriptive Statistics
- Graphical statistics
- Statistical Inference
- Hypothesis Testing
- P-Values
- One-proportion Z-test
- One-Sample T-Test
- Comparing Two Means
- The Two-Sample T-Test
- The Pooled T-Test
- Inference about Variances
- Bayesian Inference
- Regression
- Multivariate Regression
- Design and Analysis of Experiments