Math 3332: Probability Theory (Spring 2024)

General Information

Instructor: Mikhail Lavrov
Location: Mathematics 120
Lecture times: 3:30pm to 4:45pm on Tuesday and Thursday
Textbook: Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik, available online at https://www.probabilitycourse.com/. 
Office hours: Wednesday 12:00pm-2:00pm, in my office (Mathematics 245)
D2L page: https://kennesaw.view.usg.edu/d2l/home/3099360.

D2L will be used to submit assignments (these will be posted both here and on D2L, for convenience) and to view grades. The syllabus will also be posted there.

During the office hours indicated above, you should feel free to show up with no notice if you have questions of any kind. If these times do not work for you, send me an email and we'll work out something else. (Also, if you have a quick five-minute question, feel free to ask it right after class.)

Homework and Exams

There will be eight homework assignments, two midterm exams, and one final exam; the dates are marked below.

I will post the homework assignments here and on D2L; they are always due on Friday at 11:59pm, via D2L. The exception is the last assignment, which is due on Monday at 11:59pm instead.

There are also seven experimental AI-based assignments; their dates are marked here, but the assignments themselves will only be posted on D2L. These are also due on Friday at 11:59pm, via D2L, alternating weeks with the regular homework assignments. (I will say more about these assignments on the first day of class.)

Midterm exams will be given in person during our ordinary 75-minute class period.

Detailed Schedule

A note in the format "PN 1.2.3" is a reference to section 1.2.3 of the official textbook (linked above). I strongly encourage reading the textbook to supplement lectures, and working through the many problems included in the textbook.

• Date
  Topic Covered
  Other details
• Tue 1/9
  Finding probabilities
  PN 1.3.3
• Thu 1/11
  Random experiments
  PN 1.3.1-1.3.2
  AI 1 due Friday
• Tue 1/16
  No class (weather)
   
• Thu 1/18
  Models of probability
  PN 1.3.4-1.3.5
  HW 1 due Friday Sunday
• Tue 1/23
  Conditional probability
  PN 1.4
• Thu 1/25
  Law of total probability
  PN 1.4.2
  AI 2 due Friday
• Tue 1/30
  Bayes' rule
  PN 1.4.3
• Thu 2/1
  Bayes' rule: fancier examples
  PN 1.4.3
  HW 2 due Friday
• Tue 2/6
  Counting strategies
  PN 2.1.0-2.1.2
• Thu 2/8
  Binomial coefficients
  PN 2.1.3-2.1.4
  AI 3 due Friday
• Tue 2/13
  Multinomials and multisets
  PN 2.1.4
• Thu 2/15
  Random variables
  PN 3.1
  HW 3 due Friday
• Tue 2/20
  Exam 1
   
• Thu 2/22
  Expected values
  PN 3.2.2
  AI 4 due Friday
• Tue 2/27
  Special distributions
  PN 3.1.5
• Thu 2/29
  Transformations
  PN 3.2.3
  HW 4 due Friday
• Tue 3/5
  Variance
  PN 3.2.4
• Thu 3/7
  Joint distributions
  PN 5.1.1, 5.1.4
  AI 5 due Friday
• Tue 3/12
  No class
   
• Thu 3/14
  No class
   
• Tue 3/19
  Conditional distributions
  PN 5.1.3, 5.1.5
• Thu 3/21
  Sums of random variables
  PN 5.3.1, 6.1.2
  HW 5 due Friday
• Tue 3/26
  Moment generating functions
  PN 6.1.3
• Thu 3/28
  Concentration inequalities
  PN 6.2
  AI 6 due Friday
• Tue 4/2
  Exam 2
   
• Thu 4/4
  Continuous random variables
  PN 4.1.0, 4.2.1, 4.2.2
  HW 6 due Friday
• Tue 4/9
  Probability densities
  PN 4.1.1, 4.2.1, 4.2.2
• Thu 4/11
  Expected values
  PN 4.1.2, 4.2.3
  AI 7 due Friday
• Tue 4/16
  Transformations
  PN 4.1.3, 5.2.3
• Thu 4/18
  Mixed random variables
  PN 4.3.1-4.3.2
  HW 7 due Friday
• Tue 4/23
  Joint distributions
  PN 5.2
• Thu 4/25
  Poisson processes
  PN 4.2.4, 11.1
  HW 8 due Monday
• Tue 4/30
  No class
   
• Thu 5/2
  Final exam (3:30pm - 5:30pm)