Approx. 4 months
Ever played the Kevin Bacon game? This class will show you how it works by giving you an introduction to the design and analysis of algorithms, enabling you to discover how individuals are connected.
By the end of this class you will understand key concepts needed to devise new algorithms for graphs and other important data structures and to evaluate the efficiency of these algorithms.
This class assumes an understanding of programming at the level of CS101, including the ability to read and write short programs in Python; it also assumes a comfort level with mathematical notation at the level of high school Algebra II or the SATs.
See the Technology Requirements for using Udacity.
Objective: Become familiar with Algorithm Analysis.
Objective: Use mathematical tools to analyze how things are connected.
Objective: Find the quickest route to Kevin Bacon.
Objective: Learn to keep track of your Best Friends using heaps.
Objective: Work with Social Networks that have edge weights.
Objective: Explore what it means for a Social Network problem to be “harder” than other.
Interview with Peter Winker (Professor, Dartmouth College) on Names and Boxes Problem && Puzzles and Algorithms
Interview with Tina Eliassi-Rad (Professor, Rutgers University) on Statistical Measures in Network && Social Networks in Security and Protests
Interview with Andrew Goldberg (Principal Researcher, Microsoft Research) on Practical Algorithms
Interview with Vukosi Marivate (Graduate Student, Rutgers University) on Social Algorithms
Interview with Duncan Watts (Principal Researcher, Microsoft) on Pathway That Can Use Two Nodes
Intro to Graph Search Animation
Michael Littman is a Professor of Computer Science at Brown University. He also teaches Udacity’s Machine Learning courses: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Prior to joining Brown in 2012, he led the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3) at Rutgers, where he served as the Computer Science Department Chair from 2009-2012. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), served as program chair for AAAI’s 2013 conference and the International Conference on Machine Learning in 2009.
Michael has received university-level teaching awards at both Duke and Rutgers.
This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course.
This class will always be available!
Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that you’re confused, you can always go back and watch something that you skipped.
Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.
Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc… But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.
Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.