This course explores various techniques used in the design and analysis of computer algorithms. Topics covered include sorting, search trees, hashing, dynamic programming, greedy algorithms, amortized analysis, graph algorithms and basic complexity theory.
Supported by funding from the Mozilla Foundation, and working in conjunction with Georgetown's Ethics Lab, we will also be experimenting this semester with integrating into the curriculum content on ethics and social responsibility in computer science. Your participation and feedback regarding these modules will be much appreciated, as it will help shape the evolution of this innovative endeavor.
The required textbook for this course is Introduction to Algorithms (Third Edition) by Cormen, Leiserson, Rivest and Stein. Most of the topics and examples covered in this course will be adapted from this text.
This course focuses on the theoretical aspects of designing and analyzing correct and efficient algorithms. Accordingly, there are no programming projects. Problem set assignments and exams will instead require pencil and paper analysis.
In more detail, grades in this course will be based on problem sets, a midterm, and a noncumulative final.
The problem set problems will be graded on the following scale: check plus (correct answer with at most minor issues), check (shows understanding but has at least one major issue), and zero (does not demonstrate a reasonable understanding of the problem, or we could not follow your solution). Each check plus earns you 4 points, each check 2 points, and each zero, of course, 0 points. At the end of the semester, I add up all the points you earned, and this number is your problem set score.
Each problem set will be returned to you along with sample solutions, grading notes, and a guide to mapping your point score to a letter grade scale. Though this mapping will vary from problem set to problem set, depending on length and difficulty, in most cases, to earn an A on a problem set you should be aiming to get a check plus on most problems.
The midterm will be taken in class and will cover material from the first half of the class. The final exam will be taken during the final exam period and will cover material from the second half of the class. That is, the final exam is not cumulative. The midterm will be returned to you with sample solutions, grading notes, and a guide to mapping your numerical score to a letter grade.
In determining your final grade, I will add up the total points you earned on problem sets, the total points you earned on your midterm, and the total points you earned on your final. I will then map your point sum to a final letter grade.
To do so, I add to our class grading spreadsheet invented A, B, C, and D students. For each assignment and exam I give the A student the score that corresponds to the middle of the A range, the B student the score that corresponds to the middle of the B range, and so on. At the end of the class, I sum up the scores in these rows to establish a basic point range for A, B, C, and D grades. I use these to help inform the final mapping of your numerical score to a letter grade.
Notice, the total points available for the problem sets will add up to be roughly 50% of the total possible points, with the total points available on the midterms and final each adding up to roughly 25% of the available points.
If you have any questions about your current course performance please do not hesitate to talk to me. I do not want your final grade to be a surprise.
I hold regular office hours from 2:00 to 3:00 on Tuesday and Thursdays in my office at 356 Saint Mary's Hall. I'm also available for quick questions before and after class and via email at cn248@georgetown.edu. If you need a longer meeting and cannot make my regular office hours times, we can always find another meeting time that works.
This class will have graduate student teaching assistants who will also hold their own office hours to help with the problem sets. More details on their contact information and office hour times will be posted here soon.
The course schedule below describes when each problem set is assigned, when it is due, and what lectures it covers. I will post a link for downloading each problem set on the course schedule on the day the problem set is assigned. Keep in mind that the problem set schedule on the course schedule is preliminary and might shift as the semester progresses.
The following rules describe my expectations and grading policies for problem sets:
There will be two exams in the course: a midterm and a final. The final exam is not cumulative; that is, it covers material from the second half of the course.
I take academic integrity seriously. To repeat the problem set instructions from above: You can work in groups of at most three people on the problem sets. All students must write up their own answers and record on the top of the problem set any group members they worked with.
You many only discuss problems with me, your group members, and teaching assistant. The only materials you can reference when working on these problems are course notes from the current semester and the assigned textbook. In particular, you may not reference online sources or solutions from previous semesters.
You may not bring any outside material into exams.
Violating these rules will lead to a zero on the assignment and potential reporting to the academic honor council.
When in doubt, ask me what is allowed.
Below is the current schedule for the course. I might adjust this schedule as the semester progresses so please keep checking back for the latest version.
Class Number  Date  Description  CLRS Chapters 
Part 1: Algorithm Basics  
1  Thur 1/9  Introduction; ethics presurvey  1,2 
2  Tue 1/14  Tools: correctness proofs, step complexity; ethics exercise  2,3 
3  Thur 1/16  Tools: asymptotic analysis and the master method.  4 
4  Tue 1/21  Tools: probabilistic analysis and randomized algorithms  5,C 
5  Thur 1/23 
Tools: probabilistic analysis and randomized algorithms (cont)

5,C 
6  Tue 1/28 
Divide and conquer: basic technique; mergesort; ethics exercise

2,7 
7  Thur 1/30  Advanced sorting: quicksort, randomized quicksort  2,7 
8  Tue 2/4  Advanced sorting: sorting lower bounds, linear time sorting.  7,8 
Part 2: Advanced Data Structures and Design/Analysis Techniques  
9  Thur 2/6  Search trees  12 
10  Tue 2/11 
Amortized analysis

17 
11  Thur 2/13  Amortized analysis (cont.); dynamic programming (part 1)  15 
Tue 2/18  No Class: President's Day  
12  Thur 2/20 
Dynamic programming (part 2)

15 
13  Tue 2/25  Dynamic programming (part 3); greedy algorithms  15,16, 22 
14  Thur 2/27  Hashing  11 
15  Tue 3/3  Overflow; midterm review  
16  Thur 3/5  Midterm  
Tue 3/10  No Class: Spring Break  
Thur 3/12  No Class: Spring Break  
Part 3: Graph Algorithms  
17  Tue 3/17 
Minimum spanning trees

23 
18  Thur 3/19  Ethics exercise  
19  Tue 3/24  Minimum spanning trees (cont)  24,25 
20  Thur 3/26  Shortest paths  26 
21  Tue 3/31  Flows and cuts  26 
22  Thur 4/2 
Flows and cuts (cont)

34 
Part 4: Computability and Complexity Theory  
23  Tue 4/7 
Decision problems and computability

34 
Thur 4/9  No Class: Easter Break  
25  Tue 4/14  Complexity theory basics  
26  Thur 4/16  NP completeness  34 
27  Tue 4/21  NP completeness (cont); approximation algorithms  35 
Part 5: Advanced Topics  
28  Thur 4/23  Ethics program wrapup  
29  Tue 4/28  Overflow; final exam review 