From f67821f1efa3c479015c56ec1847874036e00f3c Mon Sep 17 00:00:00 2001 From: Mihir Shete Date: Wed, 25 Feb 2015 21:54:44 +0530 Subject: [PATCH 1/2] Added Fundamentals of Computer Networks from CMU INI --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1e2bdf6..ec577db 100644 --- a/README.md +++ b/README.md @@ -645,4 +645,8 @@ Topics covered include probability theory and Bayesian inference; univariate dis - Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments. - Taught by [Dr. Ching-Yung Lin](http://researcher.watson.ibm.com/researcher/view.php?person=us-chingyung) - [Course Site](http://www.ee.columbia.edu/~cylin/course/bigdata/) - - Assignments - Assignments are present in the Course Slides + - Assignments - Assignments are present in the Course Slides +- [14-740](http://www.ini740.com/F14/index.html) **Fundamentals of Computer Networks** *CMU* Lecture Videos Assignments Readings Lecture Notes + - 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. From 418a3daa91d5d96057f1627b17a0b193938d3cf8 Mon Sep 17 00:00:00 2001 From: Mihir Shete Date: Wed, 25 Feb 2015 22:00:43 +0530 Subject: [PATCH 2/2] Fix 'em tabs --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index ec577db..16a8c54 100644 --- a/README.md +++ b/README.md @@ -645,8 +645,8 @@ Topics covered include probability theory and Bayesian inference; univariate dis - Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments. - Taught by [Dr. Ching-Yung Lin](http://researcher.watson.ibm.com/researcher/view.php?person=us-chingyung) - [Course Site](http://www.ee.columbia.edu/~cylin/course/bigdata/) - - Assignments - Assignments are present in the Course Slides + - Assignments - Assignments are present in the Course Slides - [14-740](http://www.ini740.com/F14/index.html) **Fundamentals of Computer Networks** *CMU* Lecture Videos Assignments Readings Lecture Notes - - 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. + - 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.