This document outlines the course details for CS 321 Algorithm Analysis and Design. It includes information on logistics like office hours and TAs, graded work like assignments and exams, starred problems, discussion policy, and an overview of design techniques, complexity issues, and key tools covered in the course. The document also provides pre-requisites and motivates the importance of algorithms through quotes about using both theory and practice to improve each other.

1. CS 321
Algorithm Analysis & Design
Welcome to

2. Logistics
[Optional] Extra Sessions.
Office Hours: 2PM to 3PM on class days.
TAs: Shivdutt and Supratim.
Course Webpage: http://cs321.xyz
Reference material: http://algorithms.wtf

3. Graded Work
Google Code Jam Qualifiers (20%)
Self-assessment quizzes via MCQs online (25%)
Assignments on the Hacker Rank Platform (25%)
Mid-sem and End-sem Exams (30%)

4. Starred Problems
Class participation
Ungraded Work

5. Discussion Policy
Collaboration is encouraged,
but copying is forbidden.
Do the right thing!

6. Course Overview
Design Techniques
Greedy,
Dynamic Programming,
Recursion,
Divide-and-Conquer,
etc.
Complexity Issues
Why are we stuck?Can we do better?
NP-completeness
Notion of Hardness
Models of computation
etc.

7. Key Tool
Reductions
Too lazy to write findMin when sort is available.
A.sort; min(A) = A[0]

8. Design Techniques
Reduce to a solved problem.
Complexity Issues
Reduce from an un solved problem.
Key Tool

9. Story for another day
Algorithms meant for “big data”
Learning algorithms
Issues of space complexity
Coding contests and
programming interview “puzzles”

10. Pre-Requisites

11. Why bother?
Algorithms: A common language for
nature, human and computer.
Avi Wigderson

12. Why bother?
If you find that you're spending almost all your time on theory,
start turning some attention to practical things; it will improve
your theories. If you find that you're spending almost all your
time on practice, start turning some attention to theoretical
things; it will improve your practice.
Donald Knuth

13. What will I learn?
Understand the algorithms jokes at
Compare different approaches to the same problem.
Ask the right questions.
Use math to justify why you’re stuck.

14. Specification
A function from a space of inputs
to a space of outputs.

15. Can the function be
implemented by a computer?
There is no algorithm.
There exists an algorithm.
Here’s the code.

16. Here’s a problem (X)
that nobody has
solved in polynomial time.
If A is an algorithm that solves
problem Y in polynomial time,
then here’s B, an algorithm using A as a blackbox,
that solves X in polynomial time.

17. The theory of NP-completeness makes
this a very powerful statement, because
for some problems X,
solving X has serious implications.

18. What will I learn?
Understand the algorithms jokes at
Compare different approaches to the same problem.
Ask the right questions.
Use math to justify why you’re stuck.