Syllabus
-
Instructor: Dr. Fatima Taousser.
-
Office location: Min Kao building 516.
-
E-mail: ftaousse@utk.edu
-
Office hours: Friday 3:00 pm-5:00 pm or by appointment.
-
Course location: Min Kao building 622.
-
Class time: Course fully in person every Monday, Wednesday, and Friday from 9:10 am - 10:00 am.
-
Main resource: Class Notes.
-
Website: Canvas.
-
Suggested textbooks:
- R. D. Yates and D. J Goodman, ”Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers”, 3rd or 2nd edition.
- John N. Tsitsiklis and D. Bertsekas; ”Introduction to Probability”, 2nd edition, 2008.
- D. Williams, ”Probability with Martingales” (a book on the ”real”
probability theory).
-
TAs: TBA
Grading
- Homework assignments: $\approx$ 30%.
- An unannounced quiz: $\approx$ 20%.
- Midterm exam: $\approx$ 25%.
- Final exam: $\approx$ 25%.
- Some curving might be applied.
| Letter Grade |
Percentage |
| A |
90% - 100% |
| A- |
87% - 89.9% |
| B+ |
84% - 86.9% |
| B |
80% - 83.9% |
| B- |
77% - 79.9% |
| C+ |
74% - 76.9% |
| C |
70% - 73.9% |
| D |
60% - 69.% |
| F |
< 60% |
Course content
Part I: Fundamentals of Probability
- Set theory, Probability spaces.
- Permutations and Combinations.
- Conditional probability and Bayes theorem.
- Discrete random variables.
- Continuous random variables.
- Expectation, variance and higher order moments.
Part II: Notable uni-variate distributions
- Bernoulli distribution.
- Binomial distribution.
- Geometric distribution.
- Uniform distribution.
- Poisson distribution.
- Exponential distribution.
- Normal (Gaussian) distribution.
- Central limit theorem.
Part III: Notable multi-variate distributions
- Join probability distribution.
- Multivariate uniform distribution.
- Multivariate exponential distribution.
- Multivariate Gaussian distribution.
Part IV: Bayesian and Linear Estimation
- Biased and unbiased estimators.
- Maximum likelihood estimate.
- Maximum A Posteriori estimate.
- Least square estimate and linear regression.
