Data Science
DS 4420: Machine Learning and Data Mining 2
Lecture - 4 credits
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CE
- 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.
Continues with supervised and unsupervised predictive modeling, data mining, and machine-learning concepts. Show more.
Pre-requisites