Announcements
Stay current via LMS and Slack.
Lecture Materials
Lecture notes are mostly scribed by students and may contain errors or omissions. Please verify with official materials.
To contribute lecture notes:
- Clone: https://github.com/amessbee/310S26
- Create a branch from main named with your student ID for your lecture notes.
- Email (and tag) TF/TAs; post announcement to Slack channel.
Lecture Breakdown (Tentative)
Review Sessions (Tentative)
- Solving Recurrences
- Sorting Algorithms
- Graph Basics
- Minimum Spanning Trees
Grading
| Component | Weight |
|---|---|
| Exams | 20% + 25% + 30% = 75% |
| Homework Assignments | 10% |
| Classroom Worksheets | 10% |
| Slack/GitHub Participation | 5% (2+2+1) |
| Total | 100% |
Grading Scale
| Average | Assigned Grade |
|---|---|
| ≥ 90 | A category |
| 80 — 89.9 | B category |
| 70 — 79.9 | C category |
| 60 — 69.9 | D category |
| < 60 | F |
Final cutoffs determined via relative grading at semester end.
Exams
| Midterm I | 20% |
| Midterm II | 25% |
| Final Exam | 30% |
- Midterms held in-person during regular class hours.
- No make-up exams
Homework
- Use LaTeX for submissions.
- Discuss freely but write your own solutions.
- No late submissions.
- LUMS academic honesty policy strictly enforced.
- Cite all resources used.
- Be able to defend your solutions orally or in writing.
- Homework questions may appear in exams.
Classroom Worksheets
- Short exercises during lectures — participation matters.
- Almost weekly; can be done in pairs.
- No make-up for missed worksheets.
Slack/GitHub Participation
Online engagement: questions, helping peers, live commentary of work, fixing/improving lecture notes.
Weight: 2% + 2% + 1%.
Lecture Notes (0%)
Student-created notes for shared learning and clarity.
- Clone repo: https://github.com/amessbee/310S26
- Create a branch from main named with your student ID.
- Email and tag TF/TAs; post announcement to Slack channel.
Rules of the Game
- Turn off phones and electronics; be on time.
- No whispering — talk to the instructor instead; be honest.
- Register for PollEv: PollEv.com/cs310.
About Your Instructor
- Hi there! I'm Dr. Mudassir Shabbir.
- Associate Professor at CS.
- Background includes Rutgers, Vanderbilt, Los Alamos National Laboratory, ITU, Bloomberg, Punjab University.
Interests: Combinatorics, Graph Neural Networks, Discrete Geometry.
Teaching Team
CS310/5102 Teaching Club — images can be added under assets/img/ta/ and referenced as needed.
Reaching Out
Communication
- Keep up with course announcements on LMS and Slack.
- Join Slack: http://tiny.cc/slack310
- Office Hours: MW 12:00pm – 1:00pm
- Office Location: SSE 9-G47A
- Email: mudassir.shabbir@lums.edu.pk
- Always write CS310 in the subject line along with topic.
- Always CC: “Aamina Jamal Khan” <aamina.khan@lums.edu.pk> and TA team.
Suggested format
Subject: Question Regarding HW 3 Problem 1 | Discrete Structures
Dear Dr. Mudassir,
My name is Sophie (ID: BSCS23439), and I am student in your Discrete class section A. I am writing to ask for your guidance on a problem I encountered while working on the homework 3 Problem 1.
Specifically, I am having difficulty with [briefly describe the problem or the specific part of the problem you're struggling with]. I have reviewed the course materials and attempted to approach the problem in various ways, but I am still uncertain about [what exactly you are unsure about—e.g., the method, the concept, or the solution].
Could you please provide some clarification or suggest any resources that might help me better understand this issue? I would greatly appreciate any advice or direction you could offer.
Thank you for your time and assistance.
Course Overview
Rules of the Game
- Turn off phones and electronics.
- Be on time; engage by asking questions.
- Register for PollEv: PollEv.com/cs310.
- Be honest and courteous.
Grading Components
| Exams | 20% + 25% + 30% = 75% |
| Homework | 10% |
| Classroom Worksheets | 10% |
| Slack/GitHub Participation | 5% (2+2+1) |
| Total | 100% |
Outcomes
- Apply appropriate algorithmic approaches to problems.
- Analyze running time using asymptotic notation.
- Appreciate CS theory; prepare for technical interviews.
- Give/receive constructive critiques; collaborate with modern tools.
Study Tips
- Mile wide, foot deep — do not fall behind.
- Ask questions; be active.
- Practice consistently to master problem solving.
Recommended Books
Introduction to Algorithms (CLRS)
Cormen, Leiserson, Rivest, Stein — 4th Edition, MIT Press. Rigorous and comprehensive.
Algorithm Design
Jon Kleinberg, Éva Tardos — 1st Edition, Pearson. Design paradigms and intuition.
Grokking Algorithms
Aditya Bhargava — 1st Edition, Manning. Visual and beginner-friendly.