CSE100 Algorithm Design and Analysis (Fall 2015)

Staff and office hours

Instructor: Sungjin Im

TA: Maryam Shadloo

Class time and location

Lectures: Tuesdays/Thursdays 4:30-5:45pm (Classroom 113)

Lab class: Monday 7:30-10:20am (section 02L) and 10:30am-1:20pm (section 03L) (Linux Lab, SE100)

Course description

The course introduces the basics of computational complexity analysis and various algorithm design paradigms. It covers the major algorithms and data structures for searching, sorting, parsing, and memory management. Other topics include theoretical models of computation, concepts of algorithm complexity, computability, and NP-completeness.

Prerequisites: CSE31; proficient level of programming skills in C/C++/Java and elementary data structures; basic math and probability knowledge.


Required textbook (get the errata):

The companion site for the book has additional materials (partial solutions, etc.).

Other books recommended as additional reading:

Syllabus and required textbook reading


Topics: Asymptotic notation. Divide-and-conquer. Recurrent equations and the master theorem. Space and time complexity. Loop invariants. Linear and binary search. Sorting algorithms: insertion sort, selection sort, mergesort, quicksort, heapsort. Sorting lower bounds. Heaps. Binary search trees. Hash tables with chaining and open addressing. Dynamic programming and greedy algorithms. Graphs: definition and relevant problems (path search, flow, minimum spanning trees).

The course will follow parts I, II, III, IV and VI of the textbook (skipping occasional topics).

Textbook reading (table of contents):

If there is time, we will also do one of the following chapters:


Homework (to do on your own, graded): You must submit a hard copy to the instructor before the class begins on the due date. If you can't come to the class, then please email the TA your solution Cc'ing the instructor -- you should have a reasonable excuse which is subject to the instructor's approval.

Course grading

While I encourage you to discuss your work with other students, the homeworks, lab assignments, project and exams must be the result of your own work without collaboration. See the Academic Dishonesty Statement and the UC Merced Academic Honesty Policy.

Grade curves (spring 2015): midterm, final. (Note that the grade curves vary from semester to semester. In particular, this course was taught by a different instructor, Prof. Carreira-Perpiñán).



Programming resources:

Plotting resources:

Job interview questions about algorithms:

* Acknowledgements: The instructor thanks Prof. Carreira-Perpiñán for allowing me to borrow the course format including this webpage itself.