A weekly seminar focused on Manifold Learning for Machine Learning. The seminar will include an overview of Manifolds as defined in the mathematical field of topology. Some of the seminar papers will be presented. The rest of the quarter will be used to present current or widely cited papers of interest to the group.
After their very popular undergraduate-run CCS course on
Natural Language Processing, Daniel Spokoyny and Jeremy Irvin
will continue exploring their area of interest by reading central
papers on Statistical Learning Theory.
The organizational meeting will be on 4pm, Tuesday March 29 in
Phelps 3526.
All students are welcome.
This seminar will study research papers on information retrieval and web search papers.
This seminar will study recent research papers in large-scale information systems and services. The topic includes large-scale web and cloud systems and algorithms for online services/storage, data mining/serving, and computing.
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.
A weekly seminar on Network Science, with broad topics rotating amongst these different areas: socials sciences, algorithms, biological networks, dynamics and control, Cyberinfranstructure. The focus of the seminars in Spring will be on Innovation and taking research beyond academia.