New Classes for Fall 2010
The Computer Science department is offering five new classes for Fall 2010:
CS 495-02/595-07 Geospatial Vision and Visualization
Time: Tuesday 6:25 – 9:05pm
Professor: Xin Chen
Description: Geospatial information has become ubiquitous in everyday life, as evidenced by on-line mapping services such as Microsoft Virtual Earth/Bing Map, the recent addition of "place" features on social network websites such as Facebook, and free navigation on Nokia smart phones. Behind the scenes is digital map content engineering that enables all types of location-based services. Course material will be drawn from the instructor's research experience at NAVTEQ, the Chicago-based leading global provider of digital map, traffic and location data. This course will provide comprehensive treatment of computer vision, image processing and visualization techniques in the context of digital mapping, global positioning and sensing, next generation map making, and three-dimensional map content creations. Real world problems and data and on-site industry visits will comprise part of the course curriculum.
Prerequisites include linear algebra, calculus, CS 331 and C/C++.
CS 495-03 Massive Storage Architectures
Time: Thursday: 6:25 – 9:05pm
Professor: Samuel Lang
Description: This course will provide an overview of some of the largest storage systems in deployment today, including data centers at Google, Facebook, and Argonne National Laboratory, as well as others. These case studies will be used to motivate the designs, algorithms, and techniques used to provide a fault-tolerant, high- performance massive storage environment. The course will consist of class lectures and programming projects designed to teach fundamental concepts in distributed storage.
Prerequisite: CS 351
CS 595-04: Advanced Scientific Computing
Time: Thursday 6:25 – 9:05pm
Professor: Hong Zhang
Description: This course is for graduate and upper-level undergraduate students in the fields of science and engineering. The objective is to introduce the essential numerical algorithmic ideas and provide programming practice on advanced scientific computer architecture. The course contains following subjects:
- Overview of parallel computing.
- Parallel and distributed numerical computation.
- Numerical iterative techniques for solving large sparse systems.
- Numerical software design, analysis, implementation and performance evaluation, including discussions on the object-oriented programming techniques.
Students are expected to gain hands-on numerical programming experience on state-of-the-art parallel computers. By the end of the course, students are required to apply the algorithms and techniques learned in the class to projects either in their own field (particularly encouraged) or projects suggested by the instructor. Successful course project may lead to summer internship at the Argonne National Laboratory.
Prerequisites: Advanced calculus, linear algebra, background on numerical computing. Programming skill.
CS 595-05: Hot Topics in Distributed Systems: Data-Intensive Computing
Time: Monday/Wednesday, 1:50 – 3:15 pm
Location: Stuart Building 106
Office Hours: Wednesday, 3:15PM - 4:15PM, Stuart Building 237D
Professor: Dr. Ioan Raicu (firstname.lastname@example.org)
Description: The support for Data Intensive Computing is critical to advancing modern science as storage systems have experienced an increasing gap between its capacity and its bandwidth by more than 10-fold over the last decade. There is an emerging need for advanced techniques to manipulate, visualize and interpret large datasets. Building large scale distributed systems that support data-intensive computing involves challenges at multiple levels, from the network (e.g., transport, routing) to the algorithmic (e.g., data distribution, resource management) and even the social (e.g., incentives). This course is a tour through various research topics in distributed systems, covering topics in cluster computing, grid computing, supercomputing, and cloud computing. We will explore solutions and learn design principles for building large network-based computational systems to support data intensive computing. Our readings and discussions will help us identify research problems and understand methods and general approaches to design, implement, and evaluate distributed systems to support data intensive computing. Topics include resource management (e.g. discovery, allocation, compute models, data models, data locality, virtualization, monitoring, provenance), programming models, application models, and system characterization. Our discussions will often be grounded in the context of deployed distributed systems, such as the TeraGrid, Amazon EC2 and S3, various top supercomputers (e.g. IBM BlueGene/P, Sun Constellation, Cray XT5), and various software/programming platforms (e.g. Google's MapReduce, Hadoop, Dryad, Sphere/Sector, Swift/Falkon, and Parrot/Chirp). The course involves lectures, outside invited speakers, discussions of research papers, and a major project (including both a written report and an oral presentation).
CS 595-06: Probabilistic Graphical Models
Time: Tuesday/Thursday 1:50-3:05pm
Professor: Mustafa Bilgic (http://www.cs.iit.edu/~mbilgic/) Course Website:
Description: In this course, we will cover probabilistic graphical models: powerful and interpretable models for reasoning under uncertainty. We will survey a family of models, such as Bayesian networks, Markov networks, and factor graphs. The discussions will revolve around their applications to many interesting fields such as computer vision, natural language processing, computational biology, medical diagnosis, and more.
Why take this course: Probabilistic graphical models is one of the most exciting developments in machine learning. Here is a list of only a few reasons why this class might be useful and fun for you:
- Probabilistic graphical models are used everywhere - speech recognition, sensor network data modeling, computer vision, computational biology, planning under uncertainty, structured data analysis, natural language processing, and many many more.
- Course will have an application component - helps you model your own research problems using graphical models, which provides new perspectives and insights
- You will be familiar with terms and models used in an increasing number of papers - Bayesian networks, Markov networks, Conditional random fields, plate models, Gibbs sampling, belief propagation, etc.
- Experience with probabilistic graphical models is always good on your CV/resume.
More info is available at: http://www.cs.iit.edu/~mbilgic/classes/fall10/cs595/