The course covers various aspects of advanced uniprocessor computer architecture design. Instruction set architecture design alternatives are discussed with emphasis on the Reduced Instruction Set Computer (RISC) architecture. Techniques to enhance CPU performance such as pipelined execution optimizations, conditional branch handling techniques, exploitation of instruction-level parallelism, multiple-instruction issue, and dynamic scheduling are studied. Cache, and memory hierarchy design and performance issues are also presented. The design aspects of efficient and reliable input/output systems are also covered. The course concludes with an introduction to concepts of multiprocessor systems design.

Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning).