Contact

Phone: 470-578-7235

Email: mlavrov@kennesaw.edu

Office Location: D 245

Office Hours:
Check the course pages for information about office hours for my classes, or send me an email to make an appointment.

Math 3332: Probability Theory (Spring 2023)

General Information

Instructor: Mikhail Lavrov
Location: Atrium Building 109
Lecture times: 3:30pm to 4:45pm on Monday and Wednesday
Textbook: Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik, available online at https://www.probabilitycourse.com/.
Office hours:  Wednesdays 1pm-3pm and Thursdays 3pm-4pm, or by appointment, in my office (Mathematics 245)
D2L page: https://kennesaw.view.usg.edu/d2l/home/2797395.

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.

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.

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
  • Wed 1/11
    Random experiments
    PN 1.3.1-1.3.2
  • Mon 1/16
    No class
     
  • Wed 1/18
    Finding probabilities
    PN 1.3.3
  • Mon 1/23
    Models of probability
    PN 1.3.4-1.3.5
  • Wed 1/25
    Conditional probability
    PN 1.4
    HW 1 due Friday
  • Mon 1/30
    Law of total probability
    PN 1.4.2
  • Wed 2/1
    Bayes' rule
    PN 1.4.3
  • Mon 2/6
    Bayes' rule: fancier examples
    PN 1.4.3
  • Wed 2/8
    Counting strategies
    PN 2.1.0-2.1.2
    HW 2 due Friday
  • Mon 2/13
    Binomial coefficients
    PN 2.1.3-2.1.4
  • Wed 2/15
    Multinomials and multisets
    PN 2.1.4
    HW 3 due Friday
  • Mon 2/20
    Putting it all together
    PN 2.1
  • Wed 2/22
    Exam 1
     
  • Mon 2/27
    Random variables
    PN 3.1
  • Wed 3/1
    Expected values
    PN 3.2.2
    HW 4 due Friday
  • Mon 3/6
    No class
     
  • Wed 3/8
    No class
     
  • Mon 3/13
    Special distributions
    PN 3.1.5
  • Wed 3/15
    Transforming random variables
    PN 3.2.3
    HW 5 due Friday
  • Mon 3/20
    Variance
    PN 3.2.4
  • Wed 3/22
    Joint distributions
    PN 5.1.1, 5.1.4
  • Mon 3/27
    Conditional distributions
    PN 5.1.3, 5.1.5
  • Wed 3/29
    Sums of random variables
    PN 5.3.1, 6.1.2
    HW 6 due Friday
  • Mon 4/3
    Moment generating functions
    PN 6.1.3
  • Wed 4/5
    Exam 2
     
  • Mon 4/10
    Continuous random variables
    PN 4.1.0, 4.2.1, 4.2.2
  • Wed 4/12
    Probability densities
    PN 4.1.1, 4.2.1, 4.2.2
    HW 7 due Friday
  • Mon 4/17
    Expected values
    PN 4.1.2, 4.2.3
  • Wed 4/19
    Transforming random variables
    PN 4.1.3, 5.2.3
  • Mon 4/24
    Mixed random variables
    PN 4.3.1-4.3.2
  • Wed 4/26
    Joint distributions
    PN 5.2
    HW 8 due Friday
  • Mon 5/1
    Poisson processes
    PN 4.2.4, 11.1
  • Wed 5/3
    No class
     
  • Mon 5/8
    Final exam (3:30pm - 5:30pm)
     
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