Lesson 1: A Social Network Magic Trick
Objective: Become familiar with Algorithm Analysis.
- Eulerian Path
- Correctness of Naïve
- Russian Peasants Algorithm
- Measuring Time
- Steps for Naive, Steps for Russian
- Divide and Conquer
Lesson 2: Growth Rates in Social Networks
Objective: Use mathematical tools to analyze how things are connected.
- Chain, Ring and Grid Networks
- Big Theta
- Planar Graphs
- Nodes, Edges, Regions
- Growth Rate of Edges in Planar Graph
- Randomly Generated Graphs
- N Squared
- Tangled Hypercube
Lesson 3: Basic Graph Algorithms
Objective: Find the quickest route to Kevin Bacon.
- Properties of Social Networks
- Clustering Coefficient
- Connected Components
- Running Time of Connected Components
- Checking Pairwise Connectivity
- Pairwise Shortest Path
- Depth vs. Breadth First Search
- Recursion Replacement
- Marvel “Social” Network
- Finding Bridge Edges
Lesson 4: It’s Who You Know
Objective: Learn to keep track of your Best Friends using heaps.
- Degree Centrality
- Top K Via Partitioning
- Three Partitioning Cases
- Properties of a Heap
- Patch Up a Heap
- Down Heapify
- Heap Sort
Lesson 5: Strong and Weak Bonds
Objective: Work with Social Networks that have edge weights.
- Make a Tree
- Strength of Connections
- Weighted Social Networks
- How to Find the Shortest Path
- Dijkstra’s Shortest Path Algorithm
- Floyd-Warshall Intro
- Randomizing Clustering Coefficient
- Bounds on the Estimate
Lesson 6: Hardness of Network Problems
Objective: Explore what it means for a Social Network problem to be
"harder" than other.
- Exponential Running Time
- Degrees of Hardness
- Reduction: Long and Simple Path
- Polynomial Time Decidable Problems
- Non-deterministic Polynomial Time Decidable Problem
- Clique Problem in NP
- Find the Strangers
- Graph Coloring is NP-Complete
Lesson 7: Review and Application
- 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 - 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.
Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant massives des cours en ligne ouverts (MOOCs).
Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.