Tutorial-1: Machine learning and deep learning

Jinjun Xiong

Abstract: Machine learning and deep learning has attracted a lot of attention from industry, media, academia and government alike, and its impact to business and industries can't be over emphasized. The subject is broad with many on-going research and development. I plan to present an effective tutorial on such a broad subject in a two-hour duration to the DA community. My plan is to teach some of the most important fundamental techniques that are proven to be common and universal to many popular machine learning and deep learning algorithms. Covered topics will include the general iterative algorithm for solving unconstrained optimization problems, gradient descent and stochastic gradient descent methods, fundamental concepts in machine learning (such as training, testing, and cross validation, bias, variance), differences between machine learning, AI, and data mining, popular machine learning algorithms such as perceptron, logistic regression, decision tree and random forest, and deep learning algorithm such as ANN and CNN. A central theme to all the algorithmic coverage is a common set of techniques that are proven to be critical for their deep understanding. If time permits, I will also share some of my experience in applying those techniques to various industry solutions and how that relates to my deep DA roots.