Course Information

Description

Probability is a mathematical discipline for reasoning about randomness: it helps us make decisions in the face of uncertainty and build better systems. In this course, we will teach you the fundamental ideas of probability and random processes. The various assignments are carefully designed to strengthen your mathematical understanding of probability and to demonstrate how these concepts can be applied to the real world, be it in communication networks, control systems, or machine learning.

Prerequisites

Knowledge of probability at the level of CS 70 or STAT 134. Linear algebra at the level of EECS 16A or Math 54.

Course Outline

1. Fundamentals of Probability / 5 weeks
• Review: Discrete and Continuous Probability
• Bounds, Convergence of Random Variables, Law of Large Numbers
• Discrete Time Markov Chains
2. Random Processes and Estimation / 7 weeks
• Transforms, Central Limit Theorem
• Queueing, Poisson Processes, Continuous Time Markov Chains
• Communication, Information Theory
• MLE/MAP, Detection, Hypothesis Testing
3. Applications of Probability / 3 weeks
• LLSE, MMSE
• Kalman Filtering, Tracking

Textbooks

The course will follow the B&T textbook, as well as the new Walrand textbook link.

• Dimitris P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, 2nd Edition, Athena Scientific, 2008.
• Jean Walrand, Probability in Electrical Engineering and Computer Science: An Application-Driven Course, Amazon, 2020.

Piazza

We will be using Piazza for class discussion. Rather than emailing questions to the GSIs, we encourage you to post your questions on Piazza.

We will use Gradescope for all submissions/grade-related items. Note that our policy for accepting assignments (not exams) is that if Gradescope accepts the assignment, it is accepted, even if it says late or assigns a late timestamp.

The grading breakdown is as follows:

• Homework (15%)
• Lab (10%)
• Midterm 1 (20%)
• Midterm 2 (20%)
• Final (35%)

Exams

We will be using a clobber policy where your final can replace your grade for either MT1 or MT2, but not both. Exams will be proctored and in-person.

In situations that will foreseeably result in a need for accommodation, make a private Piazza post.

See the exams page for more details.

Homework

• Homeworks will be posted on the course website every Wednesday morning and are due on the following Tuesday at 11:59 PM.
• Homeworks should be submitted as a PDF to Gradescope.
• Any homework that is illegible or too difficult to read will get a 0.
• Homeworks will be self-graded through Google form. The assignments will open every Wednesday morning and due the next Tuesday at 11:59 PM.
• No late self-grades or homework submissions will be accepted.
• Your lowest homework score will be dropped automatically.
• You will have the opportunity for two extra homework drops by answering mid-semester surveys.

Labs

• Labs will be posted on the course website every Friday morning and are due on the following Thursday at 11:59 PM.
• Labs will be in the form of Jupyter notebooks. Students should submit these notebooks as a .pdf to Gradescope.
• Labs will be self-graded through Google form. The assignments will open every Friday morning and are due on the following Thursday at 11:59 PM.
• No late self grades or lab submissions will be accepted.
• Your lowest lab score will be dropped automatically.
• You will have the opportunity for two extra lab drops by answering mid-semester surveys.

Each problem is worth 4 points:

• 4: fully correct
• 3: minor mistakes, close to correct solution
• 2: major mistake/s, incorrect direction
• 1: little progress
• 0: not attempted

For problems with subparts, we will specify the breakdown; apply to rubric similarly to each subpart.

Collaboration

You are encouraged to discuss homework and lab assignments with your classmates. However, you must always write up the solutions on your own, and you must never copy the solutions of other students. Similarly, you may use books or online resources to help solve homework problems, but you must credit all such sources in your writeup and you must never copy material verbatim. You are reminded of the Departmentâ€™s Policy on Academic Dishonesty. In particular, you should be aware that copying solutions, in whole or in part, from other students in the class or any other source without acknowledgment constitutes a violation of this policy and risks serious consequences.

Policy on Course Content

The Universityâ€™s Policy on Classroom Note-Taking and Recording applies to this course. You are free and encouraged to use course materials for personal use (in collaborations with other students, in your research, etc.). You are also granted permission to post any notes you create on your own personal website. You are expressly prohibited from publicly uploading course materials created by teaching staff (exams, HW, solutions, labs). In particular, any upload of course content to websites such as CourseHero.com or Chegg.com, which distribute and monetize content without permission from the instructor or University will be considered a violation of University Policy, and referred to the Center for Student Conduct.

Course Accommodations

This semester, we are working with Michael-David Sasson and Krystle Simon for DSP accommodations. You may find the DSP extension requestion form here.