The discussions will include both the theoretical aspects of representation, learning, and inference, and their applications in many interesting fields such as computer vision, natural language processing, computational biology, and medical diagnosis. Deep networks are suitable for parallel processing implementations and can easily leverage intensive computational resources. Also, students will need to review research papers related to course topics and present a final project report at the end of the course. Design of efficient algorithms for a variety of problems, with mathematical proof of correctness and analysis of time and space requirements. Concepts and techniques for testing and analysis of software. Software development process improvement is a major objective of this course. This course is geared for junior/senior-level undergraduates and graduate students in computer science. Possible topics include: Planning: STRIPs planning; Partial-order planning; Situation calculus; Theorem proving; GraphPlan/SatPlan; Transformational planning; Simulated annealing; Motion planning; Case-based reasoning; Multi-agent coordination; Negotiation planning; Representation and Reasoning: Logical representation; Frame problem; Probabilistic reasoning; Bayesian networks; Game Playing: Minimax search; Evaluation functions; Learning evaluation functions; Markov Decision Processes; Reinforcement learning for games; Developing AI agents; Multi-agent planning. Support for metrics. A combination of analytical and experimental analysis techniques will be used to study topics such as protocol delay, end-to-end network response time, intranet models, Internet traffic models, web services availability, and network management. Intended to give the student a good basis for work in this important field. Study of simulation languages and models. Text generation. Course material will be drawn from the instructor's research and development experience at NOKIA Location and Commerce (formerly NAVTEQ), the Chicago-based leading global provider of digital map, traffic, and location data. Possible topics include: Image based modeling and rendering, Multiple view geometry, Auto-calibration, Object recognition, Motion analysis, Tracking, Perceptual user interfaces, Face and gesture recognition, Active vision. In this way deep learning simplifies learning tasks and allows using developed models to new tasks. A particular focus area will be considered, keeping current with advances in computer networking. Note: MATH 481 has prerequisites of MATH 332 or MATH 333 and MATH 475; MATH 483 has a prerequisite of MATH 476. Provide understanding of the limitations of various machine learning algorithms and the way to evaluate performance of learning algorithms. May be taken more than once. The course studies the architectures, interfaces, protocols, technologies, products and services for broadband (high-speed) multimedia networks. The hardware-software interface and the user process-system call-kernel interface are examined in detail. Hardware and software for the effective use of the computer in an educational environment, CAI (Computer-Assisted/Aided Instruction) being one of the major areas of investigation . In addition, we will examine and discuss CPS/IoT technology and market specific topics, relevant case studies of system security vulnerabilities and attacks, and mitigation controls. Elementary techniques for scanning and parsing programming languages. CS 585 Covers various topics in linguistics as they may be applied to various computational problems in AI, NLP, or IR. Current problems in computer architecture. The focus of this course is to discuss and understand the challenges in emerging cyber-physical systems and to explore possible solutions from the perspectives of systems specification, system modeling, programming languages, systems designs, and software engineering. ECE 218 and ECE 441 may also be used as computer science electives. Topics selected from mathematical systems and automata theory, decision problems, realization and minimization, algebraic decomposition theory and machines in a category.
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