Introduction to machine learning algorithms, theory, and implementation, including supervised and unsupervised learning; topics typically include linear and logistic regression, learning theory, support vector machines, decision trees, backpropagation artificial neural networks, and an introduction to deep learning. Includes a team-based project. Prerequisites: STAT 151 or STAT 251; MATH 122 or MATH 124.
Prereqs enforced by the system: STAT 151 or 251; MATH 122 or 124; Cross listed with CSYS 395 C
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (linear regression, logistic regression, neural networks, support vector machines, decision tree, ensemble models, random forest); unsupervised learning (clustering, dimensionality reduction, kernel methods); The course will also introduce deep learning such as convolutional neural networks and discuss recent applications of machine learning in addition to advices on applying machine learning algorithms.
Weekly homework, Term Project and two Mid term exams.
• Assigned homework (30%) • Term Project (30%) • Mid term exams (30%). To exams will be given during the semester, no final exam. • Class attendance and participation (10%).
Lafayette Hall L403 (View Campus Map)
to on Tuesday and Thursday
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