CS 446 ONL - Machine Learning
Theory and basic techniques in machine learning. Major theoretical paradigms and key concepts developed in machine learning in the context of applications such as natural language and text processing, computer vision, data mining, adaptive computer systems and others. Review of several supervised and unsupervised learning approaches: methods for learning linear representations; on-line learning, Bayesian methods; decision-trees; features and kernels; clustering and dimensionality reduction. Course Information: 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 373 and CS 440.
Center for Innovation in Teaching & Learning (CITL) restrictions and assessments apply, see http://citl.illinois.edu. Restricted to Engineering graduate students. Center for Innovation in Teaching & Learning (CITL) restrictions and assessments apply, see http://www.citl.illinois.edu. For more details on this course section, please see http://engineering.illinois.edu/online/courses/.
Academic Program Restrictions:
MCS: Computer Sci Online-UIUC
Option 1Number of Required Visit(s): 0
Course Level: Graduate
Term(s): Fall , Spring