2019SP_EECS_336-0_SEC1 Design & Analysis of Algorithms

2019SP_EECS_336-0_SEC1 Design & Analysis of Algorithms

EECS 336: Introduction to Algorithms

[cover] Required Text: Kleinberg and Tardos, Algorithm Design, 2005.
Discussion/Announcements: on Piazza.
Instructor Contact: send private message to Instructors on Piazza.
Homework: Logistics and Policies, Homework Guide, Peer Reviewing Guide, Canvas Issues.

Lectures: Tuesday and Thursday 9:30-10:50am in Annenberg G21.
Instructor: Jason D. Hartline.
Office Hours: Wed. 1-2pm; Mudd 3015.

Teaching Assistants: Paula Kayongo, Michalis Mamakos
Peer Mentors: Siyuan Chai, Richard Huang
Lab Sections: Monday, 

  • 10:00-10:50, Tech L168
  • 11:00-11:50, Tech L168
  • 1:00-1:50, Tech M120

Office Hours: Mudd 3532

  • Monday 2-4pm (Richard, Timothy)
  • Tuesday 4-6pm (Richard, Siyuan)
  • Wednesday 3-4pm (Michalis)
  • Friday Siyuan 2-3pm (Siyuan)

Overview. Algorithm design and analysis is fundamental to all areas of computer science and gives a rigorous framework for the study optimization. This course provides an introduction to algorithm design through a survey of the common algorithm design paradigms of greedy optimization, divide and conquer, dynamic programming, network flows, reductions, and approximation algorithms. Important themes that will be developed in the course include the algorithmic abstraction-design-analysis process and computational tractability (e.g., NP-completeness).

Prerequisites. EECS 212 (Mathematical Foundations of Computer Science) and EECS 214 (Data Structures and Data Management) which cover abstract data types such as stacks, queues, and binary search trees; and discrete mathematics such as recurrence relations, sets, and graphs.

Grading. 30% Homework, 15% Peer review, 10% Lab Sections; 30% Midterms, 15% Final.

Homework Policy. Homeworks are recommended to be done in groups of two; students must not work in groups greater than two.  Both students must contribute to the solution of all problems. Pairs should submit one typed copy of each problem to its corresponding assignment on Canvas (instructions).  Both students will receive the same grade for the submission.  Assignments must be typed and LaTeX is recommended (see LaTeX Hints). You may consult your text book and course notes when answering homework questions; you must not consult the Internet or other students except for getting ther than for help with LaTeX.   Homeworks are assigned and due on Wednesday at midnight (or as noted).  Peer reviews are assigned on Thursday morning and due Friday at 5pm.  Late homework and peer reviews will be not be accepted.  All homework problems and peer reviews will be equally weighted in your final grade with the exception of your lowest three of each which will be dropped.  See Homework Preparation Guidelines.

Tentative Schedule:

Lecture notes from a previous year are posted.  These will be updated with this years notes shortly before each lecture.

Week 1: beginning April 1:

Week 2: beginning April 8:

Week 3: beginning April 15:

Week 4: beginning April 22:

Week 5: beginning April 29:

Week 6: beginning May 6.

Week 7: beginning May 13.

Week 8: beginning May 20:

Week 9: beginning May 27:

Week 10: beginning June 3:

Week 11: beginning June 10:

  • Final: Cumulative, Thursday, 12pm-2pm.

Course Summary:

Date Details