Autumn 2026

Computer Algorithms I

Welcome to Computer Algorithms I! You can find the tentative course schedule below. As the course progresses, each day's lecture slides will be attached, as well as recommended and relevant textbook readings.
But what is this course about?

This course is about algorithms: efficient, procedural, deterministic (in this course at least) ways of solving computational problems. Broadly, we're developing your ability to efficiently solve real-world computational problems. We view this as a three-step process:

  1. Take a real-world problem and model it as a computational one, with appropriate data structures for the input and desired output.
  2. Develop a procedure for performing it efficiently.
  3. Verify that this procedure is correct and efficient.

A recurring undercurrent of this course is pushback on the idea that this process has one "correct" outcome or algorithm. Steps 1 and 3 especially are particularly design-oriented processes, and thus will always require human choices. Take Step 1, for example. Note that a "model" is, by definition, a simplification of a real thing. As an algorithm designer, you'll have to make human choices about what aspects of the real world to simplify, what the simplified representation should be, and how that impacts the algorithm you design. Or, in Step 3, we'll learn to verify that the procedure we develop is correct and efficient, but we have to decide what counts as "correct," what counts as "efficient," and whether those are even the right goals to be aiming for.

For this reason, you should not view this course as answering "how do I quickly solve this problem?" After all, AI can already do that, and before AI, search engines could already do it too. Instead, the goal of this course is for you to develop comfort thinking about and communicating about algorithms. You'll learn how to design, analyze, verify, and communicate about algorithms. You'll learn what makes them tick, where their limitations are, and what the human design choices mean for the algorithm's effectiveness. The goal is that, when it comes time to work with other people (and potentially AI) to solve the real-world problems that you're interested in, you'll have a proper seat at the table.

Syllabus Piazza Gradescope
Unit 1
Algorithm Analysis
Homework
Week 1
#01
Tue Aug 25
Introducing: Algorithms
#02
Thu Aug 27
Stable Matching
[KT 1.1], [Lecture Addendum]
Homework 0
Due Fri Sep 4, 11:59pm
Week 2
#03
Tue Sep 1
Algorithmic Complexity and Ethics
[KT 1.1], [DPV 0.3], [E 0.6]
[Worksheet]
#04
Thu Sep 3
Real-World Algorithms + Project Introduction
[Worksheet]
Homework 1
Due Fri Sep 11, 11:59pm
Unit 2
Divide and Conquer
Homework
Week 3
#05
Tue Sep 8
Mergesort and Recursive Analysis
[DPV 2.2, 2.3], [E 1.4, 1.6], [KT 5.1, 5.2]
[Worksheet]
#06
Thu Sep 10
Multiplication and Master Theorem
[DPV 2.1, 2.2], [E 1.6, 1.7, 1.9], [KT 5.5]
[Worksheet]
—
Week 4
#07
Tue Sep 15
Binary Search
[Worksheet (solutions)]
#08
Thu Sep 17
Closest Points
[E 1.6, 1.7, 1.9], [KT 5.4]
[Worksheet]
Homework 2
Due Fri Sep 25, 11:59pm
Unit 3
Graph Algorithms
Homework
Week 5
#09
Tue Sep 22
Graph Review and BFS
[DPV 3.1, 4.1, 4.2], [E 5.1, 5.2, 5.4-5.6], [KT 3.1-3.4]
[Worksheet]
#10
Thu Sep 24
DFS and Graph Decomposition
[DPV 3.2-3.4], [E 6.1-6.3, 6.5, 6.6], [KT 3.5, 3.6]
[Worksheet]
—
Week 6
#11
Tue Sep 29
Dijkstra's Algorithm and Graph Modeling
[DPV 4.4], [E 8.1, 8.6], [KT 4.4]
[Worksheet (solutions)]
#12
Thu Oct 1
Cancelled -- Instructor Illness
Homework 3
Due Fri Oct 16, 11:59pm
Week 7
#13
Tue Oct 6
Midterm Review
[Worksheet]
Thu Oct 8
Midterm Exam — in class
—
Unit 4
Greedy Algorithms
Homework
Week 8
#14
Tue Oct 13
Interval Scheduling and Greedy Proofs
#15
Thu Oct 15
Minimum Spanning Trees
—
Week 9
#16
Tue Oct 20
Topic TBD
#17
Thu Oct 22
Heuristic Selection and Analysis
Homework 4
Due Fri Oct 30, 11:59pm
Unit 5
Dynamic Programming
Homework
Week 10
#18
Tue Oct 27
Dynamic Programming I
#19
Thu Oct 29
Dynamic Programming II
—
Week 11
#20
Tue Nov 3
Bellman-Ford
#21
Thu Nov 5
Knapsack and TSP
Homework 5
Due Fri Nov 13, 11:59pm
Unit 6
Intractable Problems
Homework
Week 12
#22
Tue Nov 10
Introduction to Problem Hardness
#23
Thu Nov 12
Reductions
—
Week 13
#24
Tue Nov 17
More Reductions
#25
Thu Nov 19
NP-Completeness and P vs. NP
Homework 6
Due Fri Dec 4, 11:59pm
Week 14
#26
Tue Nov 24
Coping with NP-hard Problems
Thu Nov 26
No class — Thanksgiving
—
Week 15
#27
Tue Dec 1
Final Review
#28
Thu Dec 3
Project Presentations
—
Exam week
Dec 7 – 11
Final Exam
Time and room to be announced
Cumulative.
No homework assigned.
Lecture topics and readings are posted here before each lecture. Lecture slides are developed by me, taking strong inspiration from slides by Omar Ibrahim and the official Kleinberg-Tardos slides. My slides may contain errors.
Updated 29 Sep