[Bowdoin Computer Science]

Algorithms (CSCI 2200)
Spring 2019

Tentative Class Schedule | Department's Collaboration Policy

Basic Information

Meetings: Mon, Tues, Wed, 2:50 - 4:15 pm, Searles 126

Prerequisites: Introduction to Computer Science (CSCI 1101 or CSCI 1103) and Data Structures (CSCI 2101).

Textbook (optional): Cormen, Leiserson, Rivest and Stein, Introduction to Algorithms, 3rd Edition, MIT Press, Cambridge, MA, 2009. (errata and bugs).

Course Materials: The course homepage is http://www.bowdoin.edu/~smajerci/teaching/cs2200/2019spring/index.html and will contain all class-related material, including a tentative class schedule. I will not be using BlackBoard.

Office Hours and TA Hours

My office hours are Monday, 6:00-8:00 pm in Searles 224, and Tuesday, 1:00-2:30 pm in Searles 222. I am also available after class (within reason!) and by appointment.

We have a great group of TAs (hours TBA):

All TA hours are held in Searles 224.

Homework, Exams, and Grading

Homework: The weekly lab will contain problems to be solved in the lab, and a set that constitues the homework for the subsequent week. The homeworks will generally be due a week later (unless specified otherwise). Late assignments may not be accepted, at my discretion, unless I have received an email from your Dean describing mitigating circumstances.

Exams: There will be three in-class exams, all in Searles 126.

All exams focus on the new material that has not been covered in a previous exam, but questions about earlier material are not out of the question. The exams are closed book and closed notes, except for one 8.5 x 11 page of notes (both sides).

Grading policy: The final grade is determined as follows:

About This Course

Course Overview

Problem solving is at the heart of Computer Science and computational thinking. This class is an introduction to problem solving through the design and analysis of algorithms. I will talk about algorithm design techniques, how to argue the correctness of an algorithm, and how to analyze the efficiency of an algorithm, in the context of some fundamental algorithms. General topics include asymptotic growth, summations, recurrences, greedy algorithms, divide-and-conquer algorithms, and dynamic programming. Specific types of algorithms covered include sorting and selection algorithms, scheduling algorithms, and graph algorithms.

Course Goals

The goal of the class is to give you the tools you need to design and analyze your solutions to new problems. At the end of this class you should:

Labs

Lab time is dedicated to working through (more) examples, practice problems, and answering any questions that were not answered during lectures. A lab will usually contain a set of problems to be completed during the lab, and a problem set that becomes the homework assignment for the following week.

There will be no specific lab day. We will generally have two classes followed by a lab, but, given the calendar, this will mean that labs sometime fall on days other than Tuesday. Because of this, "Week" on the course schedule is not always a calendar week (e.g. Week 6), but rather the 3 days during which a particular topic will be covered.

The assignments will contain not only applications of the problems discussed in class, but also new problems that will require creative new ideas. Completing the assignment is a learning process (as you probably know from Data Structures). Do not expect to sit down for a few hours and solve everything at once. Instead: read the problems, understand what they are asking, come up with initial solutions, figure out why they work or not, try to formulate questions, come up with improvements. The whole process is supposed to be interactive between you, the group of people you collaborate with, your TAs, and myself.

How to Succeed

Many students find this class to be difficult. But, it is the core of computer science and it is worth all the effort you are likely to put into it. Unlike Data Structures, which focuses on using appropriate data structures to implement the solution to a problem, Algorithms focuses on designing the solution to a problem. As such, the material is more abstract. Furthermore, designing algorithms is, to a certain extent, an art as well as a science. You will learn some major algorithm design techniques, but it is not always obvious which technique will work for a given problem. Problems that seem very similar, may require different approaches.

Here are some suggestions for doing well in class:

Homework Collaboration Policy and Academic Integrity

You are expected to be familiar with and to comply with the department's collaboration policy.

Collaboration and discussion are crucial for this class. You are encouraged to work on problems in a group and you will most likely find that you will gain a better understanding of the material by discussing the problems with your partners. Note that, if you do collaborate, you need to list the names of the collaborators on the front page of the homework.

However, our goal is to ensure that the collaboration is appropriate and effective, and that you become an independent problem solver capable of working on your own. The department's collaboration policy defines four levels of collaboration. Specifically, for this class:

While peer instruction can be immensely useful, it can also be harmful. Once you have found a solution, resist giving hints to your peers or leading them towards the answer. You are not helping them by doing so. Direct them towards the TAs who are trained to give help.

Remember that you are responsible for reading, understanding, and adhering to the department collaboration policy. If you have any questions about any aspects of the policy, please do not hesitate to ask for clarification.