STAT215A
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STAT 215A - Applied Statistics and Machine Learning
Course Title
Applied Statistics and Machine Learning
Course Description
Applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on-experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods and the match between the assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods.
Minimum Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; S/U Option
American Cultures Requirement
No
Reading and Composition Requirement
None of the Reading and Composition Requirement
Prerequisites
Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended.
Repeat Rules
Course is not repeatable for credit.
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Lecture Hours Min
3
Lecture Hours Max
3
Laboratory Hours
2
Laboratory Hours Min
2
Laboratory Hours Max
2
Outside Work Hours
7
Outside Work Hours Min
7
Outside Work Hours Max
7