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Course Description

Machine learning is a sub-field of Artificial Intelligence that gives computers the ability to learn and/or act without being explicitly programmed. Applications of machine learning have permeated many aspects of every-day life and can be found among others in self-driving cars, speech recognition, computer vision, and genomics. Topics include supervised and unsupervised learning (including parametric and non-parametric models, clustering, dimension reduction, deep learning), optimization procedures, and statistical inference.

Learning Outcomes

The objective of this course is to teach fundamental methods of machine learning with focus on the theoretical underpinnings, practical implementations, and experimentation. Upon completion of the course students will:

  • Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
  • Gain an understanding of the strengths and weaknesses of many popular machine learning approaches.
  • Uncover the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning.
  • Be able to design and implement various machine learning algorithms in a range of real-world applications.

Staff

Instructor Guni Sharon
E-Mail guni@tamu.edu
Office Hours Tuesday, 10:00 -- 11:00 am
Office Zoom
TA Sumedh Pendurkar
E-Mail sumedhpendurkar@tamu.edu
Office Hours Wednesday, 3:00 -- 4:00 pm
Office Zoom