This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class.
The study of algorithms is one of the most crucial areas of in Computer Science. In this course, students will learn the basic tools of algorithm design and analysis through the study of some of the most well known and important algorithms. By the end of the semester, students will have developed not only a firm grounding in the analysis and design of algorithms, but working knowledge of the algorithms that have made computing what it is today.
There are too many algorithms topics to cover fully in a one semester course. To compensate, students will complete an in-depth research project on an algorithms-related topic of their choice, providing not only further depth but also project management skills. While this class focuses mostly on the theoretical design and analysis of algorithms, students will have opportunities to implement algorithms in the language of their choice.
This course will emphasize the first eight chapters of the main text (TADM) interleaved with selections from ADPS:
Time permitting, we’ll examine TADM chapter 9 and some basic issues in NP-Completeness and reductions.
The required course textbook is:
We will also pull some parallel algorithms material from the online textbook draft Algorithm Design: Parallel and Sequential (ADPS), by Umut Acar and Guy Blelloch, available at http://www.parallel-algorithms-book.com/. Any other required readings will be posted on this webpage as needed.
I encourage you to take advantage of the wealth of algorithms study resources that can be found online. This material can be difficult; exposure to multiple sources is always useful and sometimes necessary to fully grasp it. I will list here some resources that may be useful (and fun!). Please suggest links to add as you find them.
Late assignments: You have each been allotted a total of 5 late days. You may apply these to any problem sets you see fit and turn in your solutions with no penalty. However, you may use at most 2 on any individual assignment. The whole point here is to give you some flexibility that allows for things like illnesses, long trips, and the like. I am unlikely to grant further extensions.
Academic dishonesty: Monmouth College’s official policy on academic dishonesty can be found here. You are responsible for reading and complying with that policy.
In this course, any violation of the academic honesty policy will have varying consequences depending on the severity of the infraction as judged by the instructor. Minimally, a violation will result in an “F” or 0 points on the assignment in question. Additionally, the student’s course grade may be lowered by one letter grade. In severe cases, the student will be assigned a course grade of “F” and dismissed from the class. All cases of academic dishonesty will be reported to the Associate Dean who may decide to recommend further action to the Admissions and Academic Status Committee, including suspension or dismissal. It is assumed that students will educate themselves regarding what is considered to be academic dishonesty, so excuses or claims of ignorance will not mitigate the consequences of any violations
Collaboration: We encourage you to make use of the resources available to you – it is fine to seek help from a friend, tutor, instructor, internet, etc. However, copying of answers and any act worth of the label “cheating” is never permissible! In addition to listing your sources and collaborators, you should be producing your own writeup in your own words. By “your own words,” we mean you should be producing the text yourself, without some external aid. Verbatim copying of text is specifically disallowed, but so is taking a source and rearranging some phrases and changing some variable names to create a derivative version! Such behavior is definitely NOT “using your own words.” It does not matter if you helped contribute to this source text with others, since then you are still not the sole author of the text. The point of collaborating on an assignment is not to produce a jointly authored set of solutions, since that violates the course policies. Instead, it is to help you solve the problems, which sometimes involve a bit of creativity. After you have jointly come up with the ideas you need to solve the problems, though, you should part ways with your group and sit down to do the writing by yourself. I also advise against sharing the writeup you submit with others, since if someone else uses your text as a source for their own solution (with or without your permission), you will also be implicated in the violation of the academic integrity policy. In any case, if two nearly identical solutions are received, we have no way of tell which is the original, and the policy is to not award credit for either submission.
Electronic devices: Do not use your phone in class. Keep it on silent or leave it at home. Any computer or tablet usage should be related to the course. Other usage is rude and distracting to others.
General expectations: In short, I expect you to be respectful of others and take responsibility for your own learning. You are here to learn, so work hard and be professional.
Just attending class is not sufficient to truly learn the material. Read the text, use the resources available at Monmouth College, and go beyond the material.
If you miss class, you are responsible for everything covered on that day. College is, in some sense, your job. Take pride in creating quality work. Staple your assignments, label problems, and present your answers neatly and orderly.
Your job is to convince me that you have learned the material – show your work! Even if you do not know a particular answer, guide me through your thought process.
The course workload is as follows:
|Category||Number of Assignments||Final Grade Weight|
Generally problem sets and quizzes will alternate weeks. There will be a research projects on any algorithms-related topic, culminating in a paper and presentation to the class. The final exam will focus primarily, but not exclusively, on material covered since the midterm.
Your final grade is based on a weighted average of particular assignment categories, with weights shown above. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing.
This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:
You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge.
The weekly workload for this course will vary by student and over the semester, but on average should be about 13 hours per week. The follow table provides a rough estimate of the distribution of this time over different course components for a 16 week semester.
|Category||Total Time||Time/Week (Hours)|
The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.
Unless otherwise specified, the readings come from TADM.
|Mon 01/15||Intro to Algorithm Design||Read 1.1-1.4 (optionally 1.5-1.6)|
|Tue 01/16||Interval Scheduling, Correctness|
|Wed 01/17||Induction Examples, RAM Model||Read 2.1|
|Fri 01/19||Asymptotic Notation||PS 1 out, Read 2.2-2.7|
|Mon 01/22||Parallel Analysis||Read ADPS 1.1-1.2|
|Tue 01/23||Divide and Conquer|
|Wed 01/24||Master Theorem||Read TADM 4.5,4.10, ADPS 8.1-8.2|
|Fri 01/26||Prefix Sums (Scan)||Quiz 1, Read 8.3|
|Mon 01/29||More Scan, Contraction||Read ADPS 7|
|Tue 01/30||Faster Scan with Contraction|
|Wed 01/31||MCSS||Read ADPS 9|
|Fri 02/02||PS 2 out|
|Mon 02/05||Basic Data Structures||Read TADM 3.1-3.2|
|Tue 02/06||Dictionaries, Trees, Priority Queues||Read 3.3-3.5|
|Wed 02/07||Heaps and Heapsort||Read 4.3 (optionally 4.1-4.2)|
|Fri 02/09||Quiz 2|
|Mon 02/12||Meldable Priority Queues||Read ADPS 21|
|Tue 02/13||Leftist Heaps|
|Wed 02/14||Quicksort||PS 3 out, Read 4.6|
|Fri 02/16||Quickselect; Basic Probability||Read ADPS 10|
|Mon 02/19||Finding the Top Two||Read ADPS 11.1-11.2|
|Tue 02/20||Quickselect Analysis||Read ADPS 11.3|
|Wed 02/21||Quicksort Analysis||Read ADPS 11.4|
|Fri 02/23||Random BSTs|
|Mon 02/26||Lower Bounds on Sorting||PS 3 due, Read 4.7|
|Tue 02/27||Basic Hashing||Read 3.7|
|Fri 03/02||Midterm||PS 4 out|
|(03/05–03/09)||(Spring Break)||(Spring Break)|
|Mon 03/12||Graph Data Structures|
|Fri 03/16||Quiz 3|
|Mon 03/19||Minimum Spanning Tree|
|Wed 03/21||Shortest Path|
|Fri 03/23||PS 5 out|
|Mon 03/26||Parallel Graph Algorithms|
|Tue 03/27||Topic claiming begins|
|Wed 03/28||Quiz 4|
|(03/30–04/02)||(Easter Break)||(Easter Break)|
|Wed 04/04||Topic and Focus must be approved|
|Fri 04/06||Exhaustive Search||PS 6 out|
|Mon 04/09||Intro to Dynamic Programming|
|Wed 04/11||Edit Distance|
|Fri 04/13||More DP||Quiz 5, Anno. Bib. due|
|Tue 04/17||Max Flow|
|Fri 04/20||PS 7 out|
|(04/24)||(Scholars Day)||(Scholars Day)|
|Wed 04/25||NP-Completeness||Paper Due|
|Fri 04/27||NP Reductions|
|Mon 04/30||Presentations||Peer Review Due|
|Tues 05/08 6:30 PM||Final Exam|
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