## CSCI 532: Algorithms

### Spring 2020

Schedule subject to change. Refresh webpage (or hit F5) to view current page.
## Lecture

- Tuesday, Thursday 8:00 - 9:15 am on Webex
- Lectures will be videotaped and put on this website.

## Instructor

Sean Yaw
- E-mail: sean.yaw (at) montana.edu (email me whenever, I'll respond as soon as I get it)
- Office: Barnard Hall 360
- Office Hours: Tuesday, Thursday 9:30 - 11:00 am and by appointment.

## Textbook

- Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (3rd edition).

## Course Objectives

MSU course description: Concrete time and space complexity; combinatorial algorithms; greedy algorithms; dynamic programming; probabilistic and randomized algorithms; branch-and-bound algorithms.

At the end of the course, my goal is for you to be able to:

- Given a problem, understand it and develop a clear, efficient plan to solve it.
- Understand a broad set of algorithmic tools and have an intuition for when to apply which tools, including:
- Dynamic Programming.
- Greedy Approaches.
- Graph Representations.
- Linear Programming.
- Approximation Techniques.

- Understand and be able to comment on the time and space complexity of an algorithm, including being able to characterize recursive relations.
- Understand what NP-Complete problems are, have an intuition for the solvability of new problems, and have familiarity with techniques to deal with NP-Complete problems.

## Grading

- Homework (lowest dropped) - 40%
- Quizzes - 30%
- Presentation - 15%
- Final - 15%

At the end of the semester, grades will be determined (after any curving takes place) based on your class average as follows:

- 93+: A
- 90+: A-
- 87+: B+
- 83+: B
- 80+: B-
- 77+: C+
- 73+: C
- 70+: C-
- 67+: D+
- 63+: D
- 60+: D-
- 0+: F

## Late Policy

No late submissions will be accepted.
## Collaboration Policy

- You are encouraged to do homework assignments in groups of two people. You must indicate on the submission everyone that contributed. If someone did not substantially contribute to a submission, they cannot be included on it.
- Exams are to be taken individually.
- You may not copy or modify solutions that are not your own (e.g. from the Internet, from a classmate not listed as a contributor,...) for any graded material. I know how to use the Google and I have a Chegg membership. If you find it, I will too!

Failure to abide by these rules will result in everyone involved being reported to the Dean of Students and could result in failing the course.