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Machine Learning


About Machine Learning

This course introduces students from a variety of science and engineering backgrounds to the fundamentals of machine learning and prepares them to perform R&D involving machine learning techniques and applications. Students learn to design, implement, and evaluate intelligent systems incorporating models learned from data. On successful completion of the course, you will be able to

  1. Formulate a practical data analysis and prediction problem as a machine learning problem.
  2. Plan for data acquisition considering the characteristics of the data set required for a particular machine learning problem.
  3. Train and test supervised regression and classification models, unsupervised learning and density estimation models, and reinforcement learning models.
  4. Integrate a trained machine learning model into an online software system.


To get the most from this course, you should either have knowledge of differential calculus, linear algebra, discrete mathematics, probability and statistics, and programming in Python, OR you should be taking Mathematical Foundations of Data Science and AI and Computer Programming for Data Science and AI at the same time.

Course Staff

Matthew Dailey

Matthew Dailey (Main Instructor)

Matthew Dailey is a Professor and Head of the Information and Communication Technologies Department at the Asian Institute of Technology and the Director of the AIT AI Center. He obtained the Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego and the B.S. and M.S. in Computer Science from North Carolina State University. Before entering academia, he worked in several U.S. technology companies involved in practical applications of machine learning, including Vision Robotics Corp., Burning Glass Technologies, and HNC Software.

Manish Taneja

Manish Taneja (TA)

Manish is a Ph.D. student in Computer Science at the Asian Institute of Technology.

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