Machine learning as a field is now incredibly pervasive with several applications such as homeland security face recognition, self-driving car, social media, bioinformatics, etc. This course provides a broad introduction to machine learning, deep learning, and statistical pattern recognition. It introduces interdisciplinary machine learning techniques such as statistics, linear algebra, optimization, and computer science to create automated systems able to make predictions or decisions without human intervention. This class will familiarize students with a broad cross-section of models and algorithms for machine learning, and prepare students for research or industry application of machine learning techniques. The course also provides students with opportunities to gain hands-on experience with several machine learning tools.Offered by Info Sciences & Technology. May not be repeated for credit.
Applied Machine Learning
George Mason University
AIT 736 DL1
Emanuela Marasco (firstname.lastname@example.org)
Times and Days
Basic knowledge of probability theory, statistics, linear algebra and programming.