Computer Science 4020 (Spring 2020)

**Time and Place:** Tuesdays and Fridays,
12:00pm-1:50pm, Sage 3303

**Instructor: Elliot Anshelevich**

311 Lally Hall, 518-276-6491

**Teaching Assistants:**

Wennan Zhu, zhuw5@rpi.edu

**Office Hours:** (subject to change)

Wennan Zhu: Wednesday 3pm-4pm and Thursday 9am-10am (click here to speak with Wennan online)

Elliot Anshelevich: Tuesday and Thursday 3pm-3:50pm (click here to speak with Elliot online)

**Course Overview**

This course presents fundamental ideas and techniques of modern algorithm design and analysis. After completing this course, students should be able to analyze and design efficient algorithms for a variety of computational problems. For more details, see the Syllabus.

**Pre-requisites**

The official prerequisites for the course are CSCI 2300 and either MATH 2800 or CSCI 2200. We will assume that everyone has seen the material in these courses, and will use it as necessary.

- Specifically, from CSCI 2300 this includes elementary data structures, binary search, sorting, big-O notation, and basic terminology involving graphs (including the concepts of depth-first search, breadth-first search, and connectivity). These topics are covered in chapters 2 and 3 of the textbook. Some topics in CSCI 4020 are similar to the ones covered in CSCI 2300: we will cover them much more in-depth, and consider much more complex applications of these algorithm design techniques than in CSCI 2300.
- The prerequisites for this course also include a background in discrete mathematics, including order of function growth, sets, recurrence relations, and proof techniques such as induction and contradiction. This also includes strong and structural induction. The lectures and homework involve the analysis of algorithms at a fairly mathematical level. We expect everyone to be comfortable reading and writing proofs, at the level of MATH 2800 or CSCI 2200.

**Textbook**

The course textbook is *Algorithm Design* by Jon Kleinberg and Eva Tardos.

Although the lectures will mostly be drawn from the textbook, we will still cover things that do not appear in the text, and the textbook includes material that we will not cover in class. You are responsible for the content of the lectures as well as any assigned readings.
You may also find the following books useful for reference and for different perspectives:

- Cormen/Leiserson/Rivest/Stein: Introduction to Algorithms.
- Aho/Hopcroft/Ullman: The Design and Analysis of Computer Algorithms.
- Dasgupta/Papadimitriou/Vazirani: Algorithms.
- Garey/Johnson: Computers and Intractability.
- Kozen: The Design and Analysis of Algorithms.

**Homework, Exams, and Grading**

Students are encouraged to attend all classes. Your active in-class participation will be a substantial part of your learning process, and will be taken into consideration when final grades are determined.

**Homework.** Homework will be assigned every 1-2 weeks, and can be done in pairs. There will not be any programming assignments.
Homework should submitted on LMS, and *also* be handed in at the beginning of lecture on the day it is due.
For more information about homework, see the 4020 Homework Guide.

You are required to prove your statements, unless otherwise specified. If a homework or exam question asks you to design an
algorithm for a certain task, then the answer must consist of a description of the algorithm (an English description is fine), as well
as an analysis of its running time and a proof of its correctness.

**Exams.** There will be a midterm exam in class (see the Class Schedule for exact date), and a comprehensive final exam during finals week. All exams are open-textbook and open-notes. Make-up tests or homework assignments will not be given except in case of an emergency. Students who know they are going to miss a test must notify me in advance. Special circumstances can be accommodated if I am notified about them in advance.

**Grading.** The midterm will count for 30% of your final grade, the final for 40%, and the homework for 30%. We will drop the lowest homework grade.

*Regrades:* Any request to re-evaluate a grade must be made within one week of the return date of the homework or exam in question. You must explain why you think your grade should be changed * in writing*, and submit your request to me or a TA, together with the original problem solution. The second grade will remain. It is also your responsibility to monitor your grades on LMS; if there is any error you must bring it to my attention within one week of the grade being posted.

*Final Grades:* Final scores will correspond to the following letter grades: A/A-: 85-100; B+/B/B-: 75-85; C+/C/C-: 60-75; D+/D: 50-60.
*Exam Cutoffs:*
In addition, your maximum final grade can be at most 5 points more than your weighted exam average. So, for example, if your weighted exam average is 73 percent, then the maximum grade that you would be able to receive in the class is 78.

**Policy on Academic Integrity:** You are allowed (and encouraged) to discuss homework problems with other members of the class, and to formulate ideas together. However, everyone must write up their assignments separately, and include the names of everyone you discussed the assignment with. You may not copy (or near-copy) a solution from another, or use resources other than the class notes or the class textbook. Failure to write the solution to a homework completely on your own will be considered a breach of academic integrity, and may result in the final grade being reduced by a letter and a 0-grade for the homework for both parties. No collaboration and nothing electronic is allowed during exams.

See the Rensselaer Handbook of Student Rights and Responsibilities for more. The Rensselaer Graduate Student Supplement also defines various forms of Academic Dishonesty and procedures for responding to them. Students should make themselves familiar with this information.