Document h1610f18.pdf contains the CORRECT information about this course. This page is an electronic transformation of the Latex source used to create h1610f18.pdf using program htlatex. Content might change through the first day of classes! AFTER THE FIRST DAY OF CLASSES material will NOT BE UPDATED. CHECK THE COURSE WEB-PAGE INSTEAD for room or time changes. CS610 Syllabus by A. V. Gerbessiotis (FALL 2018).

1.1 CONTACT INFORMATION

Print Handout 1 (PDF) from Web-Page and compare it to this document! They must be identical (in content, if not in layout).

1.2 COURSE ADMINISTRATION

CourseWork: 

3 exams (including the final); 3 Homeworks (HW) ; Programming project (aka PrP).

 

Points: 1000 points = PrP(134) + Ex1(155) + Ex2(333) + Ex3(333) + HW(45)

PrP:  

A programming project (PrP) with 2 options each one worth 134 points. A student may submit one or two options; all files of one or two options in ONE archive per Handout 2 AND RECEIVED in MOODLE BEFORE NOON-TIME of the date specified in the Calendar.

HW, PS:  

Approximately six problem sets PS1-6 will be periodically posted along with their solutions. 3 practice Homeworks (HW1-HW3) with two problems will be given out (submission by moodle). PSes are not for credit; HWs are. Exams 1 and 2 may draw from these.

Exams:  

Dates in Course Calendar; all exams in classroom unless otherwise noted. Exam1 is closed-everything; if ’snow day’ it becomes Exam 2.5. Exam2 and Exam3 are open-designated-textbook only; you may bring a hard-copy of the designated textbook but you are not allowed to borrow one during the exam. Exam1, 60min, Exam2, 120min, Exam3, 120min.

Conflicts:  

As per Registrar’s Web-page. Distance learning (DL) courses might schedule final exams on the day of this class’s final exam. If you are a DL student, you should not be taking live courses i.e. this section.

1.3 BASELINE COURSE SYLLABUS

Course:  

CS610. Data structures and algorithms.

Credits:  

3 credits.

Prerequisites: 

’Discrete math’ (CS506 or CS241) and ’programming’ (CS505 or CS114).

Description:  

Intensive study of the fundamentals of data structures and algorithms. Presents the definitions, representations, processing algorithms for data structures, general design and analysis techniques for algorithms. Covers a broad variety of data structures, algorithms and their applications including linked lists, various tree organizations, hash tables, strings, storage allocation, algorithms for searching and sorting, and a selected collection of other algorithms.

Textbook(s):  

(a) [Recommended, designated] Algorithm Design: Foundations, analysis, and internet examples. M. T. Goodrich and R. Tamassia. Wiley, 2001, ISBN 0-471-38365-1.
(b) [Alternative, optional] T.C.Cormen, C.E.Leiserson, R.L.Rivest, and C. Stein. “Introduction to Algorithms”, 3rd edition, MIT Press, ISBN-10 : 0262033844 (ISBN-13: 978-0262033848),

Learning Outcomes:

1.  

Learn how and be able to understand and formulate the input-output relationship of computational problems, and formulate the requirements, data and operations of abstract data types (ADT).

2.  

Learn how and be able to asymptotically compare functions using o,O,ω,Ω,Θ, and be able to solve recurrences using the master, iteration/recursion tree, and the substitution methods.

3.  

Learn how and be able to describe, derive and determine, the asymptotic performance of algorithms for computational problems and operations on elementary and more advanced data structures.

4.  

Learn how they operate and be able to understand fundamental algorithms and data-structures, and understand their characteristics for problems related to searching, sorting, selection, operations on numbers and polynmials and matrices and graphs. Be able to choose among a variety of similar ones based on problem/program specification and requirements.

5.  

Learn how and be able to compose more complex algorithms using as building blocks the fundamental algorithms introduced in class.

6.  

Learn how and be able to compose more complex algorithms using the algorithmic design techniques introduced in class.

7.  

Learn how and be able to compose advanced data structures using as building blocks the elementary data structures introduced in class.

8.  

Learn how and be able to implement in a high-level imperative language some of the algorithms and data structures introduced in class in the form of a programming project of considerable complexity.

9.  

Learn how and be able to understand and possibly identify that some problems are complex and are not susceptible to ’easy’ solutions. Learn how and be able to understand the benefits and complexities of using randomness in computation.

Topics (with references to designated textbook):

T1. 

Ch1,5.2: Introduction, Algorithm Analysis (Asymptotic Growth of functions, recurrences).

T2. 

Ch1,4.1,5.2: Algorithm Design Techniques (Incremental, Divide-and-Conquer), Sorting.

T3. 

Ch2.1-2.3: Elementary Data Structures and Trees. Tree traverals. (Review material.)

T4. 

Ch2.4,9.3: Heaps and Priority Queues. Greedy Algorithms and Huffman coding.

T5. 

Ch4: Complexity of sorting. Distribution-based sorting. Selection. Randomization in computation.

T6. 

Ch6,Ch4.2: Graphs and their representation. Graph traversals. Strong connectivity. Topological sorting. Union-find operations.

T7. 

Not in GT: Graphs and Web-page Ranking: Google’s PageRank, Kleinberg’s HITS algorithm.

T8. 

Ch7: Weighed graph problems. Shortest-path problems. All-pairs shortest paths and transitive closure (Floyd-Warshall). Spanning trees (Prim’s and Kruskal’s algorithms).

T9. 

Ch2.5-2.7: Hashing (by chaining and open-addressing). Google Example.

T10. 

Ch5: Integer operations. The WORD and BIT models. Straight line programs. Matrix operations. Strassen’s method. Dynamic Programming and chained matrix multiplication.

T11. 

Ch3: Binary Search Trees and Balanced Binary Search trees.

T12. 

Ch9: String and Pattern matching algorithms (if time permits).

T13. 

Ch13: The complexity classes P and NP. NP-completeness.

1.4 CALENDAR

Any modification/deviation from the calendar and its items will be done in consultation with the attending a class students and be posted on the course web-page. It is imperative that students check the course web-page regularly and frequently. Exceptions are as announced by the Provost’s Office.

1.5 COURSE POLICIES

DSS:  

If you need accommodations due to a disability please contact Disability Support Services, Fenster Hall Rm 260 to discuss your specific needs. A Letter of Accommodation Eligibility from the Disability Support Services office authorizing your accommodations will be required.

MISSING:  

If you miss a class, you make up for lost time. If you miss an exam you MUST CONTACT the Dean of Students (DOS) within 2 working days from the day the reason for the absence is lifted with all necessary documentation. The maximum accommodation period will be the number of missing days to the exam date: it is imperative then that you contact DOS even before the 2 working day period has expired if the accommodation period would be shorter. For Exam1, a DOS approval will get you a scaled Exam2 grade for Exam1. No PrP extensions for any reason, medical or otherwise; you have 3 months to submit it: submit EARLY.

Devices:  

Power down and switch off (not just silence) mobile and other devices before class and place them in a bag or backpack or on the floor screen facing down. IF A STUDENT GETS CAUGHT HAVING A DEVICE (on or off) ON HIM/HER DURING an exam, the exam receives a 0. DEVICES MUST BE OFF and NOT ON YOU.

1.5 COURSE POLICIES (continued)

Grading:  

Written work will be graded for conciseness and correctness. Use formal arguments. Be brief and to the point and write clearly. Material covered in class and appearing in the relevant notes and chapters of the designated textbook can be used without proof. DO NOT USE PENCILS to write down your solutions; If you do use a pencil do not complain about grading after an exam.

PrP-grading: 

See Handout 2 for details (section Testing and Grading). On the sum of the grades of the options submitted lateness points are applied. 30 lateness points deducted at noon-time of due date and every noon-time thereafter. A 0-60 grade for the resulting grade is accounted as 0; an over-60 grade is cut-off at 134 points. Over-134 points are excess points.

Grades:  

Check the marks in a written work and report errors promptly. Resolve any issues WITHIN 2 CALENDAR WEEKS and definitely before the first Reading Day from the day an exam is returned in class or homework graded in moodle; for PrP or Exam3, within 5 calendar days from the day grades were emailed or posted on Banner respectively. Talk to the grader first and then to the instructor (if different). The final grade is decided on a 0 to 1000 point scale. A 25% or less in the final will get you an F. If you get more than 25% in the final and collect at least 500 points you should expect a C or better. 850-900 points or more are usually needed for an A including robust programming work but this varies and may be lower. If you score less than 500 points the only way to avoid an F is to do well in the cumulative final (say more than half of the points) or have excess programming points. (All these assuming no violation of the Collaboration policy.)

Incomplete:  

A grade of I(incomplete) is given in rare cases where work cannot be completed during the semester due to documented long-term illness or absence (e.g. unexpected national guard duty). A student needs to be in good standing (i.e. passing the course before the absence) and receives a provisional I if there is no time to makeup for the documented lost time; a letter (or email) with a timeline of what is needed to be done will be sent to the student. Note that for most cases an I would be resolved within few days, not months and not the following semester! Not showing up in the final will probably get you an F rather than an I.

Collaboration: 

Collaboration of any kind is PROHIBITED in the in-class exams and the PrP. For PrP, a student must turn in code that has fully been written by him/her and no-one else. Any submitted code (even few lines) obtained through the Internet or otherwise, or is product of someone else’s work or is common with another student submission, in the same or other section/course, risks severe punishment, as outlined by the University; all parties of such interaction receive automatically 0 and grade is lowered by one or two levels. The work you submit must be the result of your own mental effort and you must safeguard it from other parties; if you can’t protect your home computer, use a Lab (AFS) machine.

Email/SPAM: 

Use an NJIT email address or your email might not reach us. Send email to the designated course email address per Handout 0 instructions!