diff --git a/README.md b/README.md index 6de5318..035a83c 100644 --- a/README.md +++ b/README.md @@ -781,7 +781,7 @@ Topics covered include probability theory and Bayesian inference; univariate dis - This is an introductory course on Networking for graduate students. It follows a top-down approach to teaching Computer Networks, so it starts with the Application layer which most of the students are familiar with and as the course unravels we learn more about transport, network and link layers of the protocol stack. - As far as prerequisites are concerned - basic computer, programming and probability theory background is required. - The course site contains links to the lecture videos, reading material and assignments. -- [CS 168](https://inst.eecs.berkeley.edu/~cs168/fa15/) **Computer Networds** *UC Berkeley*Assignments Readings Lecture Notes +- [CS 168](https://inst.eecs.berkeley.edu/~cs168/fa15/) **Computer Networks** *UC Berkeley*Assignments Readings Lecture Notes - This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python). - You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.