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Nov 23, 2024
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STAT 652 - Statistical Learning Units: 2 Statistical machine learning overview. Choosing a learning algorithm. Unsupervised learning including classification methods, nearest neighbors, naïve Bayes, decision trees and rules, neural networks, k-means. Supervised learning including linear regression and logistic regression. Model performance and evaluation. Confusion matrix. Report writing.
Prerequisites: Post-baccalaureate standing. Possible Instructional Methods: On-ground, or Hybrid, or Online-Asynchronous. Grading: A-F grading only. Course Typically Offered: Spring ONLY
Student Learning Outcomes - Upon successful completion of this course students will be able to:
- Use software to learn from data.
- Critically evaluate learning models.
- Extract data from large data source to learn from data.
- Create reproducible reports.
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