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3314 Signals and Systems
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Course Time: Mon 09:10-12:00
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Classroom: AT338
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Course Outlines: This course covers fundamental concepts in signals and systems, which are broadly used for modeling, analyzing, and designing physical processes. Both the continuous-time and the discrete-time aspects will be considered. This course can be viewed as the prerequisite for the advanced courses such as linear systems, communication systems and digital signal processing. The course is outlined as:
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1. Introduction to signals and systems;
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2. Linear time-invariant systems;
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3. Fourier series representation for periodical signals;
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4. Fourier analysis for continuous-time signals and systems;
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5. Fourier analysis for discrete-time signals and systems;
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6. Sampling;
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7. Filtering;
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8. Laplace transform and Z-transform.
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Textbook: "Signals and Systems", by A. Oppenheim, A. Willsky, and H. Nawab, 2nd Edition, Prentice Hall, 1997.
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Lecture Notes: Chapter0,
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Grade:
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News:
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6654 Pattern Recognition
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Course Time: Tue 13:10-16:00
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Classroom: AT338
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Course Outlines:
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1.Introduction;
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2.Bayes Decision Theory;
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3.Maximum-Likelihood and Bayesian Parameter Estimation;
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4.Nonparametric Techniques;
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5.Multilayer Neural Networks;
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6. Deep Learning - Convolutional Neural Networks;
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7. Unsupervised Learning and Clustering.
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8. Feature Extraction - Linear Discriminant Analysis and Principle Component Analysis;
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9. Deep Learning - Autoencoder;
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Textbook: "Pattern Classification", by Richard O. Duda, Peter E. Hart and David G. Stork, John Wiley & Sons, 2nd edition, 2001.
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Reference Books:
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Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.
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Neural Networks and Learning Machines, 3rd Edition, Simon O. Haykin, McMaster University, Ontario Canada, Pearson, 2009.
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"Artificial Intelilgence" by Leonardo Araujo dos Santos. 2018.
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"Deep Learning", by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016.
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Lecture Notes: PR00,
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Grade:
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News:
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