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Welcome to the website for Sta-370, 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 8/27 Introduction Lab 1 - Crash Course in Python
T 9/1 Simple Models Finish Lab 1 Comprehensive Review of KNN
Th 9/3 Data Preprocessing Lab 2 - Introduction to sklearn
  • Lab 1 is due Friday 9/4 at 11:59pm on Canvas
  • Lab 2 is due Wednesday 9/9 at 11:59pm on Canvas
  • Homework #1 is due Tuesday 9/15 at 11:59pm on Canvas

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Week 2 - Dimension Reduction

Date Lecture Lab Resources
T 9/8 Principal Component Analysis
Th 9/10 Lab 3 - Dimension reduction via PCA
  • Concept Quiz #1 is on Tuesday 9/8 - see the last slide in each of our first three lectures for a list of possible topics
  • Lab 2 (from last week) is due Wednesday 9/9
  • Lab 3 is due Monday 9/14 Wednesday 9/16

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Week 3 - Cross-validation

Date Lecture Lab Resources
T 9/15 Cross-validation Lab 4 - Pipelines and Cross-validation
Th 9/17 Continue Lab 4 Discussion paper #1
  • Homework #1 (assigned Week 1) is due Tuesday 9/15
  • Lab 3 (from last week) is due Wednesday 9/16 - Note: you do not need to complete Question 5
  • Paper discussion #1 is on Thursday 9/17
  • Lab 4 is due Wednesday 9/23

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Week 4 - Classifier Evaluation Metrics

Date Lecture Lab Resources
T 9/22 Evaluating Classifier Performance Finish/discuss Lab 4 (from last week)
Th 9/24 Lab 5 - Classifier Performance
  • Homework #2 is due Tuesday 9/29
  • Concept Quiz #2 is on Tuesday 9/22 - it will cover PCA and cross-validation (but mostly cross-validation)
  • Lab 5 is due Monday 9/28 Wednesday 9/30

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Week 5 - More Models

Date Lecture Lab Resources
T 9/29 Random Forests Finish/discuss Lab 5
Th 10/1 Lab 6 - Ensemble Models Discussion paper #2
  • Homework #2 is due Tuesday 9/29
  • Paper discussion #2 on Thursday 10/1
    • Statistical Modeling: The Two Cultures by Breiman (2001)

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Week 6 - Gradient Boosting

Date Lecture Lab Resources
T 9/29 Gradient Boosting Lab 7 - XGBoost
Th 10/1 Finish/discuss Lab 7
  • Concept Quiz #3 is on Tuesday 10/6 - it will cover evaluation metrics and the random forests

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Week 7 - Explainability

Date Lecture Lab Resources
T 10/13 Global Importance Lab 8 - Importance Measures
Th 10/15 Local Importance Finish/discuss Lab 8 discussion paper
  • Paper discussion #3 on Thursday 10/15
    • “Why Should I Trust You?” Explaining the Predictions of Any Classifier by Ribeiro, Singh, and Guestrin (2016)

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Week 8 - Gradient Descent

  • Concept Quiz #4 is on Tuesday 10/27 - it will cover gradient boosting and model explainability

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Week 9 - Neural Networks

Week 10 - More Neural Networks

  • Paper discussion #4 on Thursday 11/12
    • ImageNet Classification with Deep Convolutional Neural Networks by Krizhevsky, Sutskever, and Hinton (2012)

Week 11 - Exam Prep

  • Review session and quiz retakes on Tuesday, exam on Thursday

Week 12 - Transfer Learning

Week 13 - Transformers

  • Paper discussion #5
    • On the Dangers of Stochastic Parrots:Can Language Models Be Too Big? by Bender, Gebru, McMillan-Major, and Shmitchell (2021)

Week 14 - Projects

  • Presentations split across Tues/Thurs

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Final Project

Coming soon…