String manipulation is a crucial part of modern software systems; for example, it is used extensively in input validation and sanitization, and in dynamic code and query generation. The goal of string-analysis techniques is to determine the set of values that string expressions can take during program execution.
We will cover a diverse state of the art topics related to data management in the cloud. These will include:
Sometimes techniques already available in one branch of science wait to be rediscovered in some disguise in another. This is particularly true for mathematics and computer science.
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