for the Spring semester of 2019-2020. The Probability theory classes are based on the 9th edition of the book of Probability and Statistics for Engineers and Scientists. The Course Syllabus contains more detailed information. The most important part is as follows:

• Attendance of classes is compulsory.
• The Office Hours at Friday 2.00pm. Registration via e-mail is required not later then 10 pm of the actual day before.

Grading

• You will receive 5-5 points at maximum for your 4 Mini quizzes and 30 points for the Final exam. Thus 50 points can be collected.
• 20 points is the limit to not to fail.
• There is no comprehensive final exam. If you retake the final exam for any reason, it will still be worth 30 points in your grading, thus it is important to do well on the quizzes, because there is no way to make up for points you loose on those. Those students who do not pass this way, may take any of the (repeat) exams, and must carry the scores of the mini quizzes. The maximum score in a repeat exam is 30 points and the above grading applies. The same rules and conditions apply to students who retake this course. All rules and conditions are regulated by the University Code of Study and Exams.
• Results after all Mini Quizzes.(Time stamp: 14:19, 10 May 2020 (CEST))

Lecture Notes

Professor Peter Tallos writes a lecture note for you. You can download his notes here: Lectures on Probability

Handouts about the Wednesday classes compiled by the instructor are available here, after each classes.

---

The week by week schedule is as follows.

1. Probability Space
   • Sample space
   • Events
   • Probability of an event
   • Binomial theorem
2. Getting started: Combinatorics
   • Counting
   • Permutations
   • Combinations
3. Conditional probability and Bayes’ rule
   • Quiz 1
   • Conditional probability
   • Product rule
   • Independent events
   • Theorem of total probability
   • Bayes’ Rule
4. Random variables and distributions
   • Bernoulli process
   • Discrete
   • Continuous
5. Expected value, and standard deviation
   • Mean
   • Variance
   • Standard deviation
6. Classical discrete distributions
   • Binomial
   • Hypergeometric
   • Geometric
   • Poisson
7. Classical continuous distributions
   • Uniform
   • Exponential
   • Standard normal
   • Normal Table of normal curve areas
8. Joint probability distributions, marginal distributions
   • Marginal distributions
   • Independency
   • Conditional expected value
9. Review