Basic concepts and techniques on data compression, error control codes, and information theoretic measures; basic concepts and techniques in statistical inference such as clustering, maximum likelihood, exact marginalization, Monte Carlo methods, importance sampling, and Markov chain Monte Carlo; introduction to neuron and neural networks; support vector machines. Prerequisites: Grade of C or better in MATH 311; grade of C or better in ECEN 303 or STAT 211; junior or senior classification Credits 3. 3 Lecture Hours.
Basic concepts and techniques on data compression, error control codes, and information theoretic measures; basic concepts and techniques in statistical inference such as clustering, maximum likelihood, exact marginalization, Monte Carlo methods, importance sampling, and Markov chain Monte Carlo; introduction to neuron and neural networks; support vector machines. Prerequisites: Grade of C or better in MATH 311; grade of C or better in ECEN 303 or STAT 211; junior or senior classification Credits 3. 3 Lecture Hours.
