Title | Introduction to Machine Learning |
Quarter | Summer 2017 (July 2017) |
Instructor | Dr Adam Ginensky (adam.ginensky@acads.org) |
Syllabus | Course Description This is a fast paced 12 week course in Machine Learning for individuals with background in Statistics and Probability. The course is an introductory course in supervised learning and will focus on Regression and Classification techniques. The concepts taught during lectures will be implemented during TA sessions. This course will use R for all programming assignments. Course Contents
At the completion of the course, students will be able to do the following:
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Instruction Format | Coursework will have following four important components:
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Assessment | A letter grade A,B,C,D or F for the course will be decided based on
Projects: 40% of the final grade
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TextBook | Introduction to Statistical Learning, James Witten, Hastie and Tibshirani |
Pre-Requisite | Prior experience with R, strong background in Statistics and Probability |
Time | Lecture Time:8:00 pm – 9:30 pm EST, Wednesday evenings Lab Time:8:00 pm – 9:30 pm EST, Monday evenings Virtual Office Hour time:TBA |
Location | Online |
TA Information | TBA |
Effort Required | 8-10 hours per week |
Certification | Participants will receive an instructor-signed certificate with a Pass grade if they score 50% and will receive Pass with distinction above 80% |
Get certificate of course completion
Attend highly interactive and hands on TA sessions and get help on projects and in-class programming sessions
Get access to course material, projects, graded homework