Probstat

This is a course web page for the course Probability and Statistics for Computer and Software Engineers.

• Facebook Group

Materials

Many of the exercises are taken from various sources, listed here: FCP = Ross, A First Course in Probability, Macmillan, 1984. SPS = Spiegel, Schiller, Srinivasan, Schaum's outlines Probability and Statistics Fourth Edition. Mc-Graw-Hill, 2013

• Week1
  • Introduction VDO: youtube
  • Coin Toss Experiments
  • Fun with statistics 1
  • Basic concepts
    • Part 1: Outcomes, Sample Spaces, and Events
    • Part 2: Conditional probability (1)
  • Random word practice 1

• Week2
  • Basic concepts
    • Part 3: Conditional probability (2) -- The clip contains errors at the end. Please see the annotations.
  • Counting
    • Part 1: Basic principles of counting
    • Part 2: Permutations and Combinations Part 2.1, Part 2.2, Part 2.3
  • Week2 Practice 1
  • Axioms of probability: youtube
  • Conditional probability: part3

• Week3
  • Class cancelled: Tuesday 9/2.
  • Conditional probability (revisited):
    • Independent events: youtube
    • Bayes' formula: part 1, part 2
  • Homework 1

• Week4
  • Random variables:
    • Part 1
    • Part 2.1 - Expectations, balls and bins experiment
    • Part 2.2 - Warm-up problems
    • Part 2.3 - First Trial to Compute the Expectation
    • Part 2.4 - The expected number of empty bins
  • Week4 Practice 1
  • Variances: Part 1, Part 2
  • Week4 Practice 2 -- E[X²].
  • (Watch this later.) Proof of the linearity of expectation: youtube

Lecture notes

These notes are meant to be used in complement to the video lectures. They only contain summary of the materials discussed in the video. Don't use them to avoid watching the clips please.

• Basic concepts
• Counting

Topics

• Course introduction
• Probability theory
  • Introduction to various concepts in probability theory
  • Elements of probability: axioms of probability, conditional probability, independence, Bayes' formula
  • Discrete random variables and expectations: random variables, expected values, variances, jointly distributed random variables
  • Tail bounds and analysis of randomized algorithms
  • Continuous random variables
• Statistics
  • Sampling
  • Parameter estimation
  • Hypothesis testing
  • Regression
  • Analysis of Variance

Administrative information

• Score breakdown: in-class activities 19%, projects 27%, midterm exam 27%, final exam 27%