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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.
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sklearn
Date | Lecture | Lab | Resources |
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Th 8/28 | Introduction | Lab 1 - Crash Course in Python | |
T 9/2 | KNN and Decision Trees | Finish Lab 1 | |
Th 9/4 | Data Pre-processing |
Lab 2 - Introduction to
sklearn
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Date | Lecture | Lab | Resources |
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T 9/9 | Finish/discuss Lab 2 | ||
Th 9/11 | Cross-validation | Lab 3 - Pipelines and Cross-Validation |
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Date | Lecture | Lab | Resources |
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T 9/16 | Assessing classifier performance | Finish/discuss Lab 3 | |
Th 9/18 | Lab 4 - Scoring Metrics |
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Date | Lecture | Lab | Resources |
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T 9/23 | Feature Transformations and Expansions | Finish/discuss Lab 4 | |
Th 9/25 | Regression for Classification | Lab 5 - Regression and Machine Learning |
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Date | Lecture | Lab | Resources |
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T 9/30 | Support Vector Machines | Lab 6 - Support Vector Machines | |
Th 10/2 | Random Forest |
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Date | Lecture | Lab | Resources |
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T 10/7 | Gradient Boosting | Lab 7 - Ensemble Models | |
Th 10/9 | Finish/discuss Lab 7 |
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Coming soon
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Coming soon
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Information about the course project will be posted here later in the semester (week 6 or 7)
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