Home Page for CMSC451 Design and Analysis of Computer Algorithms (Section 0201), Fall 2017
## CMSC451 Design and Analysis of Computer Algorithms (Section 0201), Fall 2017

Instructor: Aravind
Srinivasan

Class Venue and Time: CSIC 1122, TuTh 12:30-1:45PM

**tl;dr -- Students: please add
yourselves to the Piazza page for
the class! We will use Piazza extensively.**

## Administrative Details

**Instructor:**
Aravind
Srinivasan

**Office:** AVW 3263, **Phone:** 301-405-2695

**Instructor's Office Hours:** Tue, Thu 10-11 in AVW 3263, or by
appointment (please email Aravind)

**TAs:** Karthik A. Sankararaman (kabinav AT cs), and Pan Xu (panxu AT cs)

**TA Office Hours:** Fridays 3-4PM at AVW 4185

**Course Time and Location:** Tue, Thu
12:30-1:45, CSIC 1122

**Course Webpage:**
http://www.cs.umd.edu/class/fall2017/cmsc451-0201/index.html

## Lecture Notes and Related Resources

There is no required textbook. Instead, we will use, among others, the
following excellent publicly-available resources:
We thank the authors for these very-helpful resources.
## Aravind's Pledge to the Students

Your education is very important to me, and I respect each of you regardless
of how you do in the class. My expectations of you are that you attend class
and pay full attention, and give enough time to the course.
I strongly encourage you to ask questions in class,
and to come to the office hours (mine or the TAs')
with any further questions. We can have a
very enjoyable educational experience if you pay attention
in class, give sufficient time to our course,
and bring any difficulties you have promptly to our attention. I look forward
to our interaction both inside and outside the classroom.
## Course Overview

This course presents the fundamental techniques for designing efficient computer algorithms, proving their correctness,
and analyzing their complexity. General topics include the following;
also, *given the expected needs of Data Science (Big Data), we will
place added emphasis on probability/statistics, sublinear
algorithms, and linear algebra*.

- Graph algorithms
- Randomized algorithms
- Basic algorithm design paradigms such as
- Divide-and-conquer
- Dynamic programming
- Greedy algorithms

- Lower bounds
- NP-completeness
- Approximation algorithms
- Algorithms for Data Science (i.e., Big Data)

CMSC 351 is the prerequisite. Each student is expected to know the basic concepts of programming (e.g. loops, pointers, recursion), discrete mathematics (proof by induction, sets), simple data structures (lists, stacks, queues, trees, heaps), and calculus (logarithms, differentiation, integration).
We will assume knowledge of the basic algorithm-analysis techniques
covered in CMSC 351.
**All course materials, including the homework and project, will be
posted on Piazza.**
The coursework will consist of 5-7 homework
assignments, a small project and two exams (one midterm and a
comprehensive final). Homework problems will be mathematically oriented.

Homeworks are to be turned in at the start of class on the due date. Since
homework solutions will be handed out on the day the homework is due,
*no late homeworks will be accepted*.
All homeworks are to be done independently, with no help from the web or other sources. If you have questions, please talk to the TA or the Instructor.
Assignments are to be written up neatly.
Please staple your homework. *It is your responsibility to make sure
that you pick up all homeworks and handouts. All course information and
handouts will be available in Piazza.*

The topics, times and order listed in the syllabus next are tentative
and are subject to change:

- Graph exploration: 4-5 lectures
- Randomized Algorithms: 2 lectures
- Divide-and-Conquer algorithms: 4 lectures
- Greedy algorithms: 4 lectures
- Dynamic programming: 5 lectures
- Lower Bounds: 1 lecture
- NP-completeness and Intractability: 2 lectures
- Approximation Algorithms: 2 lectures
- Algorithms for Data Science: 2 lectures

## Topics, Schedule, and Online Resources

This course will use Piazza as a tool for communication between students, TA, and Instructor. **The schedule for the next few lectures will always be
available on Piazza; we will also have an "inverted classroom" to some extent,
where you will do assigned reading at home, with more problem-solving done in
class.** You are
**very strongly encouraged** to bring your questions to office hours. In addition, you may consider checking Piazza and posting questions there. Students are also encouraged to contribute answers to questions posed by others.
## Grading, Homework, and Exams

Final grades will be based on homework assignments, the project, the midterm exam, and the comprehensive final exam with relative weights as follows:
- Homework: 25% (Your lowest HW score will be
dropped)
- Project: 10%
- Midterm: 25% (date TBD)
- Final exam: 40%
- Date: Monday, December 18, 1:30-3:30PM

- Both exams will be held in the classroom, and will both be closed-book, closed-notes.

## Course Evaluation

Students are strongly encouraged to complete their course evaluations;
please do so at the CourseEvalUM
website when it is ready.
## Academic Accommodations for Disabilities

Any student eligible for and requesting reasonable academic accommodations
due to a disability is requested to provide, to the instructor in office
hours, a letter of accommodation from the Office of Disability Support
Services (DSS) within the first two weeks of the semester.
## Excused Absences, Academic Integrity, and Additional Info.

Please see the university's policies on various important issues.

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