Here's the topic from the first offering in 2017: http://william.cs.ucsb.edu/courses/index.php/Winter_2017_CS190I_Introduction_to_Natural_Language_Processing
but now the course has been changed into dual listing, so for the graduate students taking the course,
they need to complete an additional class project.
This seminar covers various topics related to vulnerability analysis and hacking. A deep understanding of the details of both the vulnerabilities that make security compromises possible and the countermeasures that are required to detect and block the attacks is a necessary prerequisite to address the ever-changing set of security issues that affect applications, operating systems, and networks. The seminar has a practical emphasis, and it is geared towards learning new tools and techniques in a group setting.
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.
In this seminar, the students will have the opportunities to read latest papers in the fields of natural language processing, machine learning, and artificial intelligence. The papers are typically from ACL, EMNLP, ICML, NIPS, and ICLR conferences, describing recent advances in the areas of Word Embeddings, Relational Learning, Reinforcement Learning, Generation, Dialog, and Computational Social Science etc. Each student is encouraged to lead the discussion of one paper in a quarter, and there are two papers per session.
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