3.00 Credits
Prerequisite(s): Acceptance into the Master of Computer Science program or Graduate Certificate in Artificial Intelligence program. Presents advanced models, algorithms, approaches and applications in neural networks and machine learning. Broadens and deepens the horizons of study of the philosophy and utility of machine learning models beyond what is covered in Machine Learning. Includes advanced gradient descent models, bayesian methods, boltzmann machines, recurrent neural nets, hidden markov models, randomized optimization, hopfield nets, computer vision, modern toolkits, learning from gigantic data.