DSCI/CS 372 is an introduction to Machine Learning, the subfield of Artificial Intelligence. This course will introduce the basic ideas and techniques in machine learning. The techniques you learn in this course will serve as the foundation for further study in any machine learning-related area you pursue. Students have to use Python for course projects.
Lectures
Two 80-minute lectures are delivered each week.
Prerequisites
- CS 212 - Computer Science III
- DSCI 311 - Principles and Techniques of Data Science
- DSCI 345 - Probability and Statistics for Data Science
Learning Outcomes
The learning objectives of this course are:
- To understand basic concepts and methods in Machine Learning (ML).
- To apply these ML-based methods for solving different problems.
Textbooks and readings
- (Optional) The Element of Statistical Learning, 2nd Edition by Hastie et al.
- (Optional) Pattern Recognition and Machine Learning by Christopher M. Bishop.
- (Optional) Deep Learning by Goodfellow et al.
- (Optional) Reinforcement Learning, 2nd Edition by Sutton et al.
Course Requirements and Grading
Grading will be based on the following criteria:
Percentage | Component |
|---|---|
28 | Homework |
42 | Course Project |
30 | Final Exam |
Grading Scale
>=90 | A |
>=85 | A- |
>= 79 | B+ |
>= 74 | B |
>= 68 | B- |
>= 62 | C+ |
>= 56 | C |
>= 50 | C- |
>= 47 | D+ |
>= 43 | D |
>= 40 | D- |
F |