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Figure from Learning with Kernels by Scholkopf and Smola.

 

About the Course

This is an introductory course in machine learning (ML) and pattern recognition that covers basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

ML has become one of the hottest fields of study today, and is one that is taken by undergraduate and graduate students in many universities throughout the country. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. Some of the questions that will be asked include:

  • What is learning?
  • Can a machine learn?
  • How is machine learning done?
  • How can machine learning be done well?
  • What are the traps and what are the things to look out for?
  • In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work on real data. You will learn not only about the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

    This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Learning theory (bias/variance tradeoffs, VC theory, large margins). The course will draw from numerous case studies and applications.

    A syllabus for the course may be found here.

     

    About Monson H. Hayes

    Dr. Hayes is a Professor of Electrical and Computer Engeering at George Mason University and Chair of the Department. Dr. Hayes received his Sc.D. in Electrical Engineering and Computer Science from M.I.T. in 1981 and then joined the faculty in the School of Electrical and Computer Engineering at Georgia Tech where he is currently Professor Emeritus. From 2006 until 2011, he was an Assocaite Chair for the School of ECE and Associate Director of Georgia Tech Savannah. In March of 2011, he became a Distinguished Foreign Professor at Chung-Ang University in Seoul, Korea. In the Fall of 2014, Dr. Hayes joined the faculty at George Mason University. Dr. Hayes is the recipient of the 2015 Universidad Carlos III de Madrid Chair of Excellence Award in the area of Deep Learning.

     Dr. Hayes is internationally recognized for his contributions to the field of digital signal processing. He has published more than 200 articles in journals and conference proceedings, and is the author of two textbooks, Statistical Digital Signal Processing and Modeling (Wiley, 1996), and Schaum’s Outline on Digital Signal Processing (McGraw-Hill, 1999). His research interests include DSP algorithms, signal modeling, image and video processing, face recognition, and machine learning.