Description
Provides the background in combinatorics and probability theory required in design and
analysis of algorithms, in system analysis, and in other areas of computer science.
Credits: 4
01:198:205 or 14:332:202; 01:640:152. Credit not given for this course and 01:640:477.
Please note that courses for which a student has received a grade of D cannot be used to satisfy prerequisite requirements.
Semesters Offered:Spring and fall
Topics: Counting: Binomial Coefficients, Permutations, Combinations, Partitions.
Recurrence Relations and Generating Functions.
Discrete Probability:
Events and Random Variables;
Conditional Probability, Independence;
Expectation, Variance, Standard Deviation;
Binomial, Poisson and Geometric Distributions; law of large numbers.
Some Topics from Graph Theory: Paths, Components, Connectivity, Euler Paths, Hamiltonian Paths, Planar Graphs, Trees.
Expected Work: Weekly assignments
Exams: 1 or 2 tests, Final Exam
Department Learning Goals:Computer Science majors ...
- will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
- will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
- will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).
