Official description: Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking. (Counts as a CS Background course for the concentration.)

Why should you enroll in this version of CS237?

1. Data-science approach: New for this year, the course will have a data focus. That is, many of the assignments will require you to apply the statistical approaches we learn in class to real data sets. So there will be a python programming component, that should help you start to prepare for jobs in "data science" area. See here for some info: http://www-01.ibm.com/software/data/infosphere/data-scientist/

2. Active learning: We will be using an active learning approach in this class. This means that some chalkboard lectures will be replaced with an interactive problem-solving sessions where you will work in teams to solve problems. The TF and I will be on hand to help with the problems, and to give feedback. This is also a great way to meet your classmates, and form study groups. This was done in the Fall 2012 offering of this course.

Prereqs: MA123 (or other elementary calculus class) AND CS131. We assume good working knowledge of elementary set theory and counting, and elementary calculus (i.e., integration and differentiation). These topics will be very quickly reviewed in the first weeks of the course, and are also covered in Chapters 1-2 of the Schaum's Outline text, which you can read on your own, in case you need a refresher.

Links: Course Syllabus      Piazza discussion page     Course calendar     All other course materials on blackboard