COM503: Introduction
COM503: Deep Learning is a centerpiece of the SIAI's MBA AI/BigData (AI/Finance) program, together with COM502: Machine Learning and COM504: Reinforcement Learning.
The course assumes prior knowledge in regression analysis in following courses along with important understanding of key concepts in computational efficiency (discussed in COM501: Scientific Programming) and basic understanding of computational modeling (discussed in COM502: Machine Learning and BUS501: AI in Digital Marketing )
This course is dedicated to artificial neural network models and its extensions. At first, the course covers Autoencoder, the model of which is a graph model version of nested Factor Analysis. The Lecture 3 detours to MCMC and Bayesian tools to learn Boltzmann machine in Lecture 4.
After all the basic tools are processed, the course covers CNN, RNN, and generative models, all of which are variations of basic Deep Neural Networks for specific purposes and data structures.
Final term paper continues its discussion from COM502: Machine Learning. As for COM502: Machine Learning with BUS501: AI in Digital Marketing, BUS502: AI in Business Cases is a business case course taught in parallel.