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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.

Course Materials

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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Unit #1 - Concepts, Workflow, and Methods for Tabular Data

Week 0/1 - Introduction to Python and 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
  • Labs 1 and 2 are due Monday 9/8 at 11:59pm on Canvas

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Week 2 - Cross-validation and model comparisons

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
  • Homework 1 is due Friday 9/12 at 11:59pm on Canvas
  • Labs 4 and 4 are due Monday 9/15 at 11:59pm on Canvas

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Week 3 - Regression and machine learning

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

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Week 4 - More models

Date Lecture Lab Resources
T 9/23 Support Vector Machines Lab 8 - Support Vector Machines
Th 9/25 Lab 9 - Exploring Model Limitations

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Week 5 - Ensembles

Date Lecture Lab Resources
T 9/30 Random Forests
Th 10/2 Boosting

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Unit #2 - The Mathematics of “Learning”

Coming soon

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Unit #3 - Introduction to Deep Learning

Coming soon

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Final Project

Information about the course project will be posted here later in the semester (week 6 or 7)

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