I am very glad to introduce you with the three NYU graduate courses available now in my OpenStudy project:
Computational Photography: Computational photography draws my interests not only because people in this area develope algorithms that produce many amusing results, but also because it is a greatly applicable science that requires a good possession of mathematics in a broader sense. It intersects closely to other computer science and eletronic engineering disciplines such as machine learning and digital signal processing.
Machine Learning and Pattern Recognition: The course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. It covers the mathematical methods and theoretical aspects, but will primarily focus on algorithmic and practical issues. Machine Learning and Pattern Recognition methods are at the core of many recent advances in "intelligent computing". Current applications include machine perception (vision, audition, speech recognition), control (process control, robotics), data mining, time-series prediction (e.g. in finance), natural language processing, text mining and text classification, bio-informatics, neural modeling, computational models of biological processes, and many other areas.
Operating Systems: The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.
As always, I am very glad to share with you my experience of studying computer science. If you have any suggestions to the OpenStudy project, don't hesitate to contact me!
