Although deep learning is based on many well known artificial intelligence (AI) concepts dating back decades or more, it has come to prominence again based on the number of recent successes. A combination of improved algorithms, increased access to data, and the availability of low-cost/high-performance computing resources have dramatically improved the performance of deep learning for certain application areas.
This course will focus on implementation and applications of deep learning systems for tasks such object recognition, speech recognition, language processing, and autonomous driving. Specific concepts will include neural networks, deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) as well as improvement and optimization techniques such as back propagation, gradient decent, parameter tuning and regularization. This course will involve extensive hands-on programming assignments. Comfort programming in a high level languages such as Matlab or Python is required as well a solid understanding of linear algebra.
Pre-reqs: One of MATH 152, MATH 221, and one of MATH 318, MATH 302, STAT 302, STAT 321, ELEC 321, and one of CPEN 221, CPEN 223, CPSC 259