Welcome to this PG level course.

- Class timings: Monday 12:00-12:55, Tuesday: 10:00 - 11:55, Thursday: 08:00-08:55
- Venue: MS Teams
- Instructor: Prof. Ashish Hota

### Course Content (Tentative)

Here is a basic overview of the topics that are planned to be covered.

- Introduction to Probability Theory and Random Variables (2 weeks)
- Joint and Conditional Distributions, Conditional Expectation, Limit Theorems (2 weeks)
- Static Estimation: Maximum Likelihood Estimation, Maximum Aposteriori Estimation, Minimum Mean Square Estimation, Principle of Orthogonality, Linear Mean Square Estimation (2 week)
- Dynamic Estimation: Kalman Filter (2 weeks)
- System Identification (2 weeks)
- Case Study (1 week)
- Optional: Stochastic Processes: Stationarity, Ergodicity, Second-order theory (2 weeks)

### Reference Materials

There is no single textbook for this subject. We will discuss a variety of topics from different books. The first reference below will be a standard text for most of the course. You are encouraged to refer the other texts below depending on your interests. Of course there are several advanced texts on probability theory, random processes, estimation theory, statistical signal processing and system identification, but we will mostly be content with the following.

- Random Process For Engineers by Bruce Hajek, a preprint of the book is available here.
- Stochastic Processes, Estimation and Control by Jason Speyer and Walter Chung. Available in library.
- Probability and Random Processes by Geoffrey Grimmett and David Stirzaker. Contains lots of numerical and conceptual problems.
- Stochastic Systems: Estimation, Identification, and Adaptive Control by P. R. Kumar and Pravin Variaya. Another classic text.
- System Identification: Theory for the User by Lennart Ljung. This is a standard textbook for system identification.