Course Staff
| Instructors |
Prof. Sofya Raskhodnikova Prof. Tiago Januario |
| Teaching Fellows |
Anatoly Zavyalov Erick Jimenez Berumen |
| Course Assistants |
Steve Choi Letitia Caspersen Daniel Matuzka Vi Tjiong Sarah Yuhan Yoon Oh |
Communication and Office hours
- Piazza is the primary platform for all online discussions, questions, and answers.
- Use private posts for sensitive or specific questions regarding your solutions or personal matters.
- Do not send emails to the course staff.
- We aim to maintain a positive and inclusive atmosphere on all course platforms.
- If you need special accommodations or are facing unusual circumstances during the semester, please reach out to an instructor privately via Piazza as early as possible.
- For accommodations, forward relevant documentation from Disability and Access Services.
- Your suggestions for improving the course are always encouraged and appreciated.
- Check our Google Calendar for hour Office hours.
Prerequisites
We assume good working knowledge of elementary set theory and counting, elementary calculus (i.e., integration and differentiation), and programming in Python.
Syllabus
Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking. This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking.
Course structure
- Attendance in lectures and discussion is mandatory
- The two lecture sections of the course will be treated as one class.
- The content of the two lectures is identical, and assignments will be shared.
Textbooks
You can access both books for free or support the authors by purchasing the books.
- (LLM) Mathematics for Computer Science by Eric Lehman, Tom Leighton, and Albert Meyer.
- (P) Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik.
- (CLRS) Cormen, Leiserson, Rivest, and Stein. Introduction to Algorithms, Fourth Edition, MIT Press.
Schedule
This schedule is subject, and likely, to change as we progress through the semester. Reading chapters are from the first textbook (LLM) or from the second textbook (P), referred to by the acronyms of the author names.
| Date / Lec | Agenda (Topics, Readings, Homework) | Instr. |
|---|---|---|
|
Lec 1 Tue, Sep 02 |
TJ & SR | |
|
Lec 2 Thu, Sep 04 |
hw01 out
|
TJ |
|
Lec 3 Tue, Sep 09 |
SR | |
|
Lec 4 Thu, Sep 11 |
hw02 out
|
SR |
|
Lec 5 Tue, Sep 16 |
TJ | |
|
Lec 6 Thu, Sep 18 |
hw03 out
|
TJ |
|
Lec 7 Tue, Sep 23 |
TJ | |
|
Lec 8 Thu, Sep 25 |
hw04 out
|
TJ |
|
Lec 9 Tue, Sep 30 |
SR | |
|
Lec 10 Thu, Oct 02 |
hw05 out
|
SR |
|
Lec 11 Tue, Oct 07 |
Last Day to Drop Standard Courses (without a “W” grade)
|
SR |
|
Lec 12 Thu, Oct 09 |
hw06 out
|
SR |
| Tue, Oct 14 |
Substitute Monday Schedule of Classes
Check the Google Calendar for the updated office hour schedule
|
|
|
Lec 13 Thu, Oct 16 |
Midterm practice problems out
|
SR |
|
Lec 14 Tue, Oct 21 |
Midterm practice solutions out
|
SR |
| Thu, Oct 23 |
Midterm, in class, during lecture time
Covers all topics up to Lecture 13
hw07 out
|
|
|
Lec 15 Tue, Oct 28 |
TJ | |
|
Lec 16 Thu, Oct 30 |
Infinite sums
Linearity of expectation
hw08 out
|
TJ |
|
Lec 17 Tue, Nov 04 |
Law of the unconscious statistician
Conditional expectation Linearity of conditional expectation |
TJ |
|
Lec 18 Thu, Nov 06 |
Law of total expectation
Variance Standard deviation Variance properties
hw09 out
|
TJ |
|
Lec 19 Tue, Nov 11 |
Discrete distributions: Bernoulli, Uniform, Binomial
|
SR |
|
Lec 20 Thu, Nov 13 |
Discrete distributions: Geometric and its properties
hw10 out
|
SR |
|
Lec 21 Tue, Nov 18 |
Coupon collector's problem
Reservoir sampling Negative Binomial |
SR |
|
Lec 22 Thu, Nov 20 |
Markov inequality
Chebyshev inequality
hw11 out
|
TJ |
|
Lec 23 Tue, Nov 25 |
Applications of Markov and Chebyshev's inequalities
Continuous Uniform Distribution |
TJ |
| Thu, Nov 27 | Thanksgiving break | |
|
Lec 24 Tue, Dec 02 |
Normal Distribution
|
TJ |
|
Lec 25 Thu, Dec 04 |
Exponential Distribution
Course evaluation
CLRS 8.4
hw12 out
Final Practice Problems out |
SR |
|
Lec 26 Tue, Dec 09 |
Poisson Distribution
Course evaluation
Final Practice Solutions out
|
SR |
| Thu, Dec 11 | Study period begins | |
|
Final Exam Dec 18 |
Final Exam
Time: 3:00 PM – 5:00 PM
Location: HAR 105
Cumulative and covers all topics
|
We have prearranged the following date and time for students with final exam conflict:
- Alternative Final exam date: December 16th
- Time: 3:00 PM – 5:00 PM
- Location: STHB19
Students with exam accommodations, regardless or their final exam conflict, will take the final exam at same day and initial time of the other students at CDS 801.
Participation and Attendance
- Participation in lecture will be tracked with Top Hat questions.
- Participation in discussion labs will be tracked with Top Hat location-based attendance.
- You will get the full participation points if you answer at least 85% of the possible Top Hat questions.
- You will get the full attendance points if you attend at least 85% of the discussion labs.
- If you end up with x% points, where x < 85, you will get x/85 of the points.
- Most of the material covered in lectures and labs can be found in our textbooks. Read them!
- While our textbook will be very helpful, it is an imperfect substitute for in-class learning, which is the fastest (and easiest) way to learn the material.
- In all cases, you are responsible for being up to date on the material.
Homework Policy
- Submission: Weekly assignments are posted on Thursdays and due by Wednesdays at 9:00 PM ET via Gradescope. A 3-hour grace period is allowed; submissions after this period will not be accepted. Missing submissions will be treated as late.
- Content: Provide step-by-step explanations for your answers. Submissions with only answers will receive minimal credit.
- Formatting: Submit solutions as one single PDF file with high-quality images. Illegible submissions will receive a 0. We recommend using Dropbox for scanning.
- Gradescope Page Selection: Correctly select the pages for each problem on Gradescope. Failure to do so will result in a 10% penalty. If you don't have a solution, note "No solution provided."
- Late Policy: You may use up to 3 grace periods without penalty. After that, each late submission incurs a 1% penalty. The lowest homework grade will be dropped after penalties are applied.
- Academic Integrity and Collaboration:
- The Collaboration & Honesty Policy outlines the rules of collaboration and penalties for cheating. All students are required to read, sign, and submit this document to Gradescope.
- Submitting identically worded answers, including pseudocode, is a serious offense and will be reported to the Dean's Office (BU Academic Conduct Code).
- Using ChatGPT or similar AI for homework solutions violates the Collaboration & Honesty Policy.
- You are not required to cite material from the course textbook, lectures, discussion notes, or information provided by course staff.
- For any other external information, a proper citation is required. Failure to cite constitutes plagiarism.
- Explicitly searching for problem answers online or from individuals not enrolled in the current semester's class is strictly forbidden.
- If you receive help on Piazza or during office hours from instructors for specific problems, you must list them as collaborators.
- Partial Submissions: Submitting partial work is acceptable if you cannot fully complete an assignment; avoid missing the deadline entirely.
Exams
- Both exams will consist of problem-solving and short questions about the material.
- Each exam duration and their locations are given in the course schedule.
- The content of the final is cumulative.
- No collaboration whatsoever is permitted on exams, any violation will be reported to the College.
Grading
The course grade will break down as follows:
- 5% class participation
- 5% lab attendance
- 25% weekly homework assignments
- 30% in-class midterm exam
- 35% in-class final exam. Don't make any travel plans before the final date is released.
- Incompletes for this class will be granted based on CAS Policy.
- Note: Optional homework problems do not count towards your course grade (except when you are right on the border), but we will look at how you did on them if you ask for a recommendation letter.
Regrade Policy
- Regrade requests can be submitted up to one week (7 days) after grades for a given assignment have been posted (except the final exam).
- You must request a regrade via Gradescope, NOT through email.
- When we regrade a problem, your score may go up or down.
Miscellaneous
LaTeX resources
- TexShop is a latex editor for the Mac platform;
- TexNiCenter is a text editor for Windows;
- Overleaf is a web-based latex system (that allows you to avoid latex installation on your machine).
- Not so short intro to latex;
- A latex tutorial.