Course

Goals

This course covers advanced topics in AI and Machine learning, the“A” part of the core ABC (AI model/algorithm, Big data, Computing) courses in data science. This course covers the advanced topics of machine learning and deep learning for data scientists. Topics covered in machine learning part include unsupervised learning (clustering, dimension reduction), graphical models (Bayesian networks, Markov Random Field), and basic reinforcement learning. In deep learning part, models dealing with sequence data such as RNN, Attention model, and Transformer are covered with various types of data, (images, videos, or text). Lastly, basic generative models (Variational Autoencoder, GAN) will be covered.