Statistics & Probability for CS Students

Description

This course introduces fundamental concepts in probability and statistics, tailored for computer science students. Through a combination of theory and practical exercises, students learn how to model and analyze uncertainty in real-world scenarios. Emphasis is placed on applying counting techniques, random variables, and distributions to solve problems that arise in computing and data analysis. Learning is reinforced through practice with real-world applications.

Content

Session 1: Introduction

Session 2: Counting & Combinatorics I

Session 3: Counting & Combinatorics II

Session 4: Probability

Session 5: Conditional Probability & Bayes I

Session 6: Conditional Probability & Bayes II

Session 7: Independence

Session 8: Random Variables, Expectation & Variance

Session 9: Correlation & Covariance

Session 10: Bernoulli and Binomial Random Variables

Session 11: Poisson Random Variables

Session 12: Other Discrete Distributions

Session 13: Recap

Session 14: Continuous Distributions I

Session 15: Continuous Distributions II

Session 16: Normal Distributions I

Session 17: Normal Distributions II

Session 18: Central Limit Theorem

Session 19: Beta Random Variables

Session 20: Gamma Random Variables

Session 21: Confidence Intervals & Qunatiles I

Session 22: Confidence Intervals & Qunatiles II

Session 23: Recap