Teaching modes (remote, hybrid, in-person) are subject to change at any point. If this happens, faculty will be in contact with students enrolled in their courses.

To determine if a course is remote, hybrid, or in-person use the catalog search tool to narrow results. Otherwise, when browsing courses, the section indicates teaching mode:

R = Remote
H = Hybrid
0 = In-person

STAT 315
Applied Machine Learning Spring 2021
Division III Quantative/Formal Reasoning

Class Details

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.
The Class: Format: lecture
Limit: 30
Expected: 20
Class#: 4205
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: weekly homework, one class project, and two or three exams
Prerequisites: MATH 140, and STAT 201/202, or equivalent; or permission of instructor
Enrollment Preferences: Statistics majors, seniors
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: This is a statistics class with a focus on mathematical, computational, and data analysis skills as well as appropriate practical application of analysis methods

Class Grid

Updated 3:45 am ET

Course Catalog Search


(searches Title and Course Description only)
TERM




TEACHING MODE
SUBJECT
DIVISION



DISTRIBUTION



ENROLLMENT LIMIT
COURSE TYPE
Start Time
End Time
Day(s)