INTRODUCTION TO DATA SCIENCE AND MACHINE LEARNING: FOUNDATIONS AND APPLICATIONS

Course Description

Understanding how AI / Machine Learning systems perform tasks by generalizing from training data poses interesting challenges and problems for mathematical analysis and for the further development of methods. This course covers both rigorous mathematical theory for the foundations of machine learning and practical approaches for developing methods. The course will draw on topics in statistics, analysis, optimization, computation, and related areas. Methods discussed will include kernel methods, deep learning, recent types of neural network architectures, and other approaches. A central emphasis of the course is on mathematical theory and practical techniques that can be used in guiding the design of machine learning algorithms, perform training, or carrying out analysis to help understand observed performance. The beginning introductory materials of the course will use the book “Foundations of Machine Learning,” by M. Mohri, A. Rostamizadeh. The course topics will also draw on recent developments in the literature and the research work of the instructor.

Optional Text

M. Mohri, A. Rostamizadeh, Foundations of Machine Learning 2nd Ed, (available on-line from author)