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How does Netflix recommend films based on your viewing history? How does Facebook group its users and send out targeted ads? How did Google select from thousands of search terms to predict flu? Machine learning (ML) is a rapidly growing field that is concerned with algorithms and models to find patterns in data and solve these practical problems at the intersection between statistics, data science and computer science. This course provides a broad introduction to ideas and methods in machine learning, with emphasis on statistical intuitions and practical data analysis. Topics including regularized regression, SVM, supervised/unsupervised learning, text analysis, neural networks will be covered. Students will use R extensively throughout the course while getting introduced to some ML tools in Python.
Format: lecture; Hybrid format. Students cannot take both STAT 315 and STAT 442. Only one of the two can be taken for credit.
Grading: yes pass/fail option,
yes fifth course option
weekly homework, one class project, and two or three exams
MATH 140, and STAT 201/202, or equivalent; or permission of instructor. Students cannot take both STAT 315 and STAT 442. Only one of the two can be taken for credit.
This is a statistics class with a focus on mathematical, computational, and data analysis skills as well as appropriate practical application of analysis methods