Course number may be repeated for credit when the topics vary.
Topic: 5 - Engineering Programming Languages Higher-level languages for engineering design and problem solving; object-oriented programming in C++ and Unix systems programming.
Topic: 6 - Operating Systems Input/output systems calls, drivers and descriptors, and integrated circuits. Design and implementation of hardware and software for a Unix-like operating system.
Topic: 7 - Introduction to Pattern Recognition and Computer Vision Pattern recognition topics, including Bayesian decision theory, maximum likelihood and estimation, nonparametric techniques, and linear discriminant functions. Computer vision topics, including geometric camera models and calibration, geometry of multiple views and stereopsis, structure from motion, and tracking. Emphasis varies each semester.
Topic: 8 - Computer Vision Systems Discussion of current research results and exploration of new directions in computer vision systems. Includes linear discriminant functions, nonmetric methods, unsupervised learning and clustering, model-based vision, segmentation using probabilistic methods, and content-based image and video analysis. Application of the techniques to real-world vision systems. Emphasis varies each semester.
Topic: 9 - Artificial Neural Systems Feed-forward networks, distributed associative memory, recurrent networks, self-organization, parallel implementation, and applications. Topic: 10 - Data Mining Analyzing large data sets for interesting and useful information. Includes online analytical processing, finding association rules, clustering, classification, and function approximations. Scalability of algorithms and real-life applications.
Topic: 10 - Data Mining - SE Analyzing large data sets for interesting and useful information. Includes online analytical processing, finding association rules, clustering, classification, and function approximations. Scalability of algorithms and real-life applications.
Topic: 11 - Mining the Web Analysis of data and information available from the World Wide Web. Exploiting the hyperlink structure of the Web for developing better search engines. Content analysis, information retrieval, clustering, and hierarchical categorization of Web documents. Web usage mining. Collaborative filtering and personalizing the Web. Additional prerequisite: Electrical Engineering 380L (Topic 10: Data Mining) or Computer Sciences 391L.