Course Syllabus,
CS 5521 Data Structures and Algorithms
Fall 2015

Course Data:
Instructor: Doug Dunham
Email: ddunham@d.umn.edu
Web Site: http://www.d.umn.edu/~ddunham
Office:333 Heller Hall
Phone:726-7510
Office Hours: T, Th 2-3 p.m., W 2-4:50 p.m., and by appointment
Lectures: Tu, Th 3:30-4:45 p.m. in MWAH 175
Lab: W 5-5:50 p.m. in MWAH 177
Course Web Site: http://www.d.umn.edu/~ddunham/cs5521f15

Teaching Assistant: Anicia D'Costa
Email: dcost004@d.umn.edu
Web Site: http://www.d.umn.edu/~dcost004
Office Hours: Mon. 1-2 pm and Wed. 11am-noon in HH 314, and Wed. 4-5 pm in MWAH 177

Bulletin Description:
Survey of advanced data structures and algorithms such as heaps and heapsort, quicksort, red-black trees, B-trees, has tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis.

Prerequisites:
CS 2511, CS 3512, Math 3326 or instructor consent, a grade of C- or better is required in all prerequisite courses.
Important note: The computer science bachelor's degree program at UMD is accredited by CAC (the Computing Accreditation Commission). One of the CAC requirements is that all students must satisfy the prerequisites in order to be admitted to a course, so if you have not passed the prerequisite courses, you must drop this course (if you have any questions about this, please see the instructor after the lecture or during office hours).

Course Objectives and Content:
Advanced computer programming requires an understanding of both the objects on which programs work, the data structures, and the sequences of steps driving the programs themselves, the algorithms. In order for data structures and algorithms to be generally useful, they often must overcome memory and speed limitations of the underlying machine. Thus it is important for the programmer to be able to analyze algorithms for their efficiency in execution and use of space for data. This course will study the data structures and algorithms with an eye toward both analyzing efficiency and developing working models using sound data abstraction and procedural abstraction practices.

The following is a rough outline of the material that I hope to cover in the course. We will start with a discussion of algorithms and their analysis and design. This will be followed by a review of growth of functions and solving recurrences. Then we will discuss most of the following topics: advanced sorting, binary search trees, red-black trees, B-trees, hash tables, binary heaps and priority queues, binomial heaps, Fibonacci heaps, amortized analysis, graph algorithms, minimal spanning trees, and shortest path algorithms. Additional material may also be covered, such as dynamic programming, greedy algorithms, string matching, encryption, NP-completeness, and approximate algorithms.

Course Objectives and Student Learning Outcomes:

  1. Students will gain knowledge of the core methods of advanced data structures such as use of appropriate data structures and algorithms, and analysis of their running times.
    Expected Outcomes:
    1. The students will demonstrate use of those data structures and algorithms in lab and homework assignments.
    2. The students will demonstrate knowledge of those data structures and algorithms on exam questions.
  2. Students will gain knowledge of key issues in advanced data structures, such as appropriateness of data structures and efficiency of their related algorithms.
    Expected Outcomes:
    1. The students will demonstrate use of those key concepts in lab and homework assignments.
    2. The students will demonstrate knowledge of those key concepts on exam questions.
  3. Students will gain proficiency in applying knowledge from the theory of advanced data structures to various application areas.
    Expected Outcomes:
    1. The students will demonstrate that proficiency in lab and homework assignments.
    2. The students will demonstrate that knowledge on exam questions.

Equal Opportunity:
The University of Minnesota is committed to the policy that all persons shall have equal access to its programs, facilities, and employment without regard to race, color, creed, religion, national origin, sex, age, marital status, disability, public assistance status, veteran status, or sexual orientation. As instructor, I am committed to upholding University of Minnesota's equal opportunity policy. I encourage you to talk to me in private about any concerns you have related to equal opportunity in the classroom. To inquire further about the University's policy on equal opportunity, contact the Department of Human Resources & Equal Opportunity 255 DAdB, (http://www.d.umn.edu/umdoeo), phone: (218) 726-6827, email: umdeo@d.umn.edu.

Students with Disabilities:
It is the policy and practice of the University of Minnesota Duluth to create inclusive learning environments for all students, including students with disabilities. If there are aspects of this course that result in barriers to your inclusion or your ability to meet course requirements - such as time limited exams, inaccessible web content, or the use of non-captioned videos - please notify the instructor as soon as possible. You are also encouraged to contact the Office of Disability Resources, 258 Kirby Student Center, to discuss and arrange reasonable accommodations. Please call 218-726-6130 or visit the DR website at (http://www.d.umn.edu/access) for more information.

Mental Health Statement:
As a student you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may lead to diminished academic performance or reduce a student's ability to participate in daily activities. University of Minnesota services are available to assist you with addressing these and other concerns you may be experiencing. You can learn more about the broad range of confidential mental health services available on campus via the UMD Health Service Counseling website at http://www.d.umn.edu/hlthserv/counseling/.

Student Conduct Code:
Appropriate classroom conduct promotes an environment of academic achievement and integrity. Disruptive classroom behavior that substantially or repeatedly interrupts either the instructor's ability to teach, or student learning, is prohibited. Student are expected adhere to Board of Regents Policy: Student Conduct Code: ( http://www.d.umn.edu/conduct/ ).

Teaching and Learning: Instructor and Student Responsibilities:
UMD is committed to providing a positive, safe, and inclusive place for all who study and work here. Instructors and students have mutual responsibility to insure that the environment in all of these settings supports teaching and learning, is respectful of the rights and freedoms of all members, and promotes a civil and open exchange of ideas. To reference the full policy please see: http://www.d.umn.edu/vcaa/TeachingLearning.html

Student Academic Integrity Policy:
Academic dishonesty tarnishes UMD's reputation and discredits the accomplishments of students. Academic dishonesty is regarded as a serious offense by all members of the academic community. This course will adhere to UMD's Student Academic Integrity Policy, which can be found at http://www.d.umn.edu/conduct/

Final Exams:
All 1xxx-5xxx courses offered for undergraduate credit should include a final graded component or end of term evaluation that assesses the level of student achievement of one or more course objectives. All final graded components are to be administered or due at the time and place according to the final exam schedule and not during the last week of class. To reference the full policy please see: http://www.d.umn.edu/vcaa/FinalExams.html

Excused Absences:
Students are expected to attend all scheduled class meetings. It is the responsibility of students to plan their schedules to avoid excessive conflict with course requirements. However, there are legitimate and verifiable circumstances that lead to excused student absence from the classroom. These are subpoenas, jury duty, military duty, religious observances, illness, bereavement for immediate family, and NCAA varsity intercollegiate athletics. For complete information, please see: http://www.d.umn.edu/vcaa/ExcusedAbsence.html

Appropriate Student Use of Class Notes and Course Materials:
Taking notes is a means of recording information but more importantly of personally absorbing and integrating the educational experience. However, broadly disseminating class notes beyond the classroom community or accepting compensation for taking and distributing classroom notes undermines instructor interests in their intellectual work product while not substantially furthering instructor and student interests in effective learning. For additional information, please see: http://www.d.umn.edu/vcaa/ClassNotesAppropriateUseof.html

Text:

Cormen, T. H., C. E. Leiserson, R. L. Rivest, and C. Stein Introduction to Algorithms, Third Edition, 2009, MIT Press, Cambridge, MA, ISBN: 978-0-262-03384-8 (hardcover) or 978-0-262-53305-8 (paperback). Web site: http://mitpress.mit.edu/algorithms and the list of corrections is here.

Course Requirements:

The lab assignments must be done in C or C++ (without using the Standard Template Library), not Java (since the algorithms have been implemented in Java on the CD that comes with some printings of the text).

It is not directly required that you attend class, however: You are responsible for reading assigned text material and for material covered in class and in the lab, including:

  1. doing reading assignments from the text
  2. the material covered in the lectures
  3. obtaining assignments and handouts
  4. turning in programming assignments and homework

If you are unable to attend a class meeting, it is your responsibility to obtain class notes, assignments, and extra copies of handouts from your study partner. Note: assignments are due at the beginning of class on the due date (unless otherwise specified) -- they will be docked 25% per day if turned in late.

Assignments:
There will be weekly assignments which will consist of both programming and non-programming problems. Programming assignments will be based on algorithms written in a generic pseudo-code presented in lecture and in the text. The solutions must be written in C or C++ (without using stl), as mentioned above. C++ is particularly appropriate for implementing data structures. The programming assignments should adhere to the Computer Science Lab Report Format; the non-programming assignments should adhere to the Written Homework Format.

Examinations and Grading:

There will be a midterm exam, worth 100 points and a final exam worth 200 points. These exams are closed book, but you are allowed to bring one 4 x 6 inch note card to the midterm exam as a crib sheet, and two such cards to the final exam. The final exam will be comprehensive. Exams will not be given early, and makeups must be justified by dire circumstances described to the instructor before the time of the exam. It is Department of Computer Science policy not to return final exams, however they are kept and you can look at your exam in the instructor's office. The UMD Final Examination Policy web page explains the UMD policy about having more than two final exams on a single day, among other things.

Exam Schedule:
ExamPointsDate and Time
Midterm Exam 100 points Thursday, October 22, 3:30-4:45 pm in MWAH 175
Final Exam 200 points Wednesday, December 16, 2-3:55 pm in MWAH 175

Scores and total points will be maintained by the TA on eGradebook.

Grading Procedures: Final grades are based on total points distributed approximately as follows:

Grades are assigned based on a percentage of the total points. These percentages may be lowered slightly but they will not be raised.
Important note: In the past, students have tended to do better on the lab and homework assignments (average = 78%) than on the midterm (73%) and final (68.5%) exams. Thus class averages going into the final exam tend to be higher than after the final exam. This leads to the following two pieces of advice: (1) keep your average as high as possible by doing well on the lab/homework assignments, and (2) study carefully for the exams, using the review guides, so that your average isn't brought down by poor exam scores.
The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the University of Minnesota.
Page URL: http://www.d.umn.edu /~ddunham/cs5521f15/syllabus.html
Page Author: Doug Dunham
Last Modified: Tuesday, 01-Sep-2015 17:00:08 CDT
Comments to: ddunham@d.umn.edu