Topics
Data Representation
Representation of Text
Representation of Images and Video
Representation of Sound and Music
Storing and Accessing Data
Iteration
Recursion
Universal Computation
Arithmetic Algorithms
Searching and Sorting
Advanced Algorithms
Analytical versus Numerical
Symbolic Computation
Everyday Cryptography
This is new gradaute-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/book2016June9.pdf
The course will primarily focus on the following topics:
Geometry of high-dimensionsl space
Matrix methods
Machine learning
Clustering
Graph models
The course work will consist of both programming and theory projects.
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.
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.
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.
Students should have taken at least one programming language class and one networking class (CS176, CS276), and have experiences in doing software projects of reasonable size/complexity.
The course itself will combine, lectures, assigned reading, and in-class discussion with a quarter-long software development project that attempts to familiarize the student with the strengths and weaknesses of current approaches to cloud computing. In addition, students will be expected to describe the research challenges they have identified as a result of their experiences in the course in both written and oral presentation formats.
Specific topics the course will cover include
-- Infrastructure as a Service (IaaS)
-- Platform as a Service (PaaS)
This is a graduate-level course on neural network and deep learning. We will discuss neural network, deep learning, CNN, RNN, LSTM and their newest advances.
This course will explore Network Science concepts in the context of social and biological networks. Topics covered include social structure theory, evolution of biological networks, management of Big Data, and visualization of networks.
NOTE: Same course as Winter 2015.
Enterprise systems are large-scale software applications to support
business operations; they typically include software systems for data
management, business process/workflow management, information flows,
reporting, and data analytics. Focusing only the data management
aspect, a typical enterprise has to struggle with many data
integration difficulties, since its data are usually spread around
many database systems, workflow systems, file systems, etc. and in a
variety of form possibly with no coherent semantics. In this course,
Internet access is so critical it has been declared a “human right” by the United Nations. In developing nations Internet access can critically influence socio-economic advancement, and can enable democratic change, wide-spread education and improved health care. In times of emergency Internet access provides vital, and at times life-saving, information. Despite its value to society, 60% of the global population still lacks Internet access due to geographic remoteness and issues of affordability.