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|DS 4420 - Machine Learning and Data Mining 2|
Continues with supervised and unsupervised predictive modeling, data mining, and machine-learning concepts. Covers mathematical and computational aspects of learning algorithms, including kernels, time-series data, collaborative filtering, support vector machines, neural networks, Bayesian learning and Monte Carlo methods, multiple regression, and optimization. Uses mathematical proofs and empirical analysis to assess validity and performance of algorithms. Studies additional computational aspects of probability, statistics, and linear algebra that support algorithms. Requires programming in R and Python. Applies concepts to common problem domains, including spam filtering.
4.000 Credit hours
4.000 Lecture hours
Schedule Types: Lecture
Data Science Department
NUpath Analyzing/Using Data, Computer&Info Sci