This is new graduate-level course on mathematical foundations of data science, based on the forthcoming book Foundations of Data Science by Avrim Blum, John Hopcroft and Ravi Kannan. The current draft of the book is available at http://www.cs.cornell.edu/jeh/
The course will primarily focus on a number of fundamental topics including
Geometry of high-dimensional space
Matrix methods
Machine learning
Clustering
Graph models
Data stream processing
This course covers advanced topics on information retrieval, web search, and related scalable information systems. The topics include search engines and advertisements, web crawling, classification, indexing and data serving, ranking and recommendation, user behavior analysis, and online services. This course will also cover system and middleware support for building related large-scale Internet services.
Topics:
Deep learning has revolutionized many subfields within AI. DeepMind's AlphaGo combined convolutional neural networks together with deep reinforcement learning and MCTS, and won many games against top human Go players. In computer vision, most of the leading systems in ImageNet competitions are based on deep neural networks. Deep learning has also changed the game in NLP: for example, Google has recently replaced their phrase-based machine translation system with neural machine translation system.
This class is meant to open to you research in Cryptography, both theoretical and applied. To do so, the class will involve reading research papers, reviewing them, discussing them, and doing a project.
Possible topics include:
• Graph algorithms o Traversals
292F: All About Networks Fall 2017, TR 9-11, Phelps 2510
o Shortest paths o Spanning tree o Network flow o Matching
Spectral analysis
o Eigenvaluesandeigenvectors o Laplacian
o Conductance bounds
Cuts, partitions, and sparsifiers
Random walks
Metrics:
o Centrality
o Homophily
Power laws
Network models
o Erdos Renyi (ER) model
Cryptography provides techniques, mechanisms, and tools for private and authenticated communication, and for performing secure and authenticated transactions over the Internet as well as other open networks. It is highly probable that every single bit of information flowing through our networks will have to be either encrypted and decrypted or signed and authenticated in a few years from now.
This graduate-level special topic class will cover emerging topics on data-driven networking and systems design, ranging from data center networking, mobile networking/computing, cloud computing, infrastructure for social networks, Internet of Things, and cyber physical systems. Students will read and present papers from recent top conferences, and do a team project.