\(~\)
Welcome to the website for Sta-395, Introduction to Machine Learning! To begin, you can find the course syllabus linked below:
You can locate course content by scrolling, or by using the navigation bar in the upper-left.
Most class meetings involve both lecture and lab components. Topics are organized into units, which can be found below. The assignments and due-dates for a given week can be found below that week’s course materials. Unless otherwise indicated, all assignments are to be submitted via Canvas by 11:59pm.
\(~\)
sklearn
Date | Lecture | Lab | Resources |
---|---|---|---|
Th 8/28 | Introduction | Lab 1 - Crash Course in Python | |
T 9/2 | KNN and Decision Trees | ||
Th 9/4 | Data Pre-processing |
Lab 2 - Introduction to
sklearn
|
\(~\)
Date | Lecture | Lab | Resources |
---|---|---|---|
T 9/9 | Cross-validation | Lab 4 - Hyperparameter tuning using cross-validation | |
Th 9/11 | Classifier Performance | Lab 5 - Comparing classifiers |
\(~\)
Date | Lecture | Lab | Resources |
---|---|---|---|
T 9/16 | Regression and Machine Learning | Lab 6 - Feature Transformations and Expansions | |
Th 9/18 | Regularization | Lab 7 - LASSO, Ridge, and Elastic Net |
\(~\)
Date | Lecture | Lab | Resources |
---|---|---|---|
T 9/23 | Support Vector Machines | Lab 8 - Support Vector Machines | |
Th 9/25 | Lab 9 - Exploring Model Limitations |
\(~\)
Date | Lecture | Lab | Resources |
---|---|---|---|
T 9/30 | Random Forests | ||
Th 10/2 | Boosting |
\(~\)
Coming soon
\(~\)
Coming soon
\(~\)
Information about the course project will be posted here later in the semester (week 6 or 7)
\(~\)