CSCI 532: Algorithms
Spring 2021
Schedule subject to change. Refresh webpage (or hit F5) to view current page.
Lecture
- Tuesday, Thursday 8:00 - 9:15 am in Cheever 214
- 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
- Webex: link
- Office Hours (in my office and on Webex): 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) - 50%
- Quizzes - 35%
- Presentation - 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.
Sickness Accommodations
- This entire course can be taken remotely. Lectures are posted online. Office hours can be done remotely. Homework is turned in online. Tests will be given online.
- Do not come to class (or office hours) if you are sick.
- Do not use your homework drop early. You never know when you will get sick and need to use it.
Masks
Per MSU:
"Face coverings are required in all indoor spaces and all enclosed or partially enclosed outdoor spaces. MSU requires all students to wear face masks or cloth face coverings in classrooms, laboratories and other similar spaces where in-person instruction occurs. MSU requires the wearing of masks in physical classrooms to help mitigate the transmission of SARS-CoV-2, which causes COVID-19. The MSU community views the adoption of these practices as a mark of good citizenship and respectful care of fellow classmates, faculty, and staff.
The complete details about MSU’s mask requirement can be found at https://www.montana.edu/health/coronavirus/index.html."