\(~\)

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.

\(~\)

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 Finish Lab 1
Th 9/4 Data Pre-processing Lab 2 - Introduction to sklearn
  • Lab 1 is due Monday 9/8 at 11:59pm on Canvas
  • Lab 2 is due Wednesday 9/10 at 11:59pm on Canvas

\(~\)

Week 2 - Cross-validation and model comparisons

Date Lecture Lab Resources
T 9/9 Finish/discuss Lab 2
Th 9/11 Cross-validation Lab 3 - Pipelines and Cross-Validation
  • Homework 1 is due Friday 9/12 at 11:59pm on Canvas
  • Lab 3 is due Wednesday 9/17 at 11:59pm on Canvas

\(~\)

Week 3 - Classifier performance

Date Lecture Lab Resources
T 9/16 Assessing classifier performance Finish/discuss Lab 3
Th 9/18 Lab 4 - Scoring Metrics
  • Homework 2 is due Monday 9/29 at 11:59pm on Canvas
  • Lab 4 is due Wednesday 9/24 at 11:59pm

\(~\)

Week 4 - Regression and machine learning

Date Lecture Lab Resources
T 9/23 Feature Transformations and Expansions Finish/discuss Lab 4
Th 9/25 Regression for Classification Lab 5 - Regression and Machine Learning
  • Lab 5 is due Wednesday 10/1 at 11:59pm

\(~\)

Week 5 - Regularization

Date Lecture Lab Resources
T 9/30 Regularization Finish/discuss Lab 5
Th 10/2 Lab 6 - Regularized Regression
  • Lab 6 is due Wednesday 10/8 at 11:59pm
  • Homework 3 is due Friday 10/17 at 11:59pm
    • Benchmark scores for this assignment will be released soon \(~\)

Week 6 - More Models

Date Lecture Lab Resources
T 10/7 Support Vector Machines Lab 7 - Support Vector Machines An Idiot’s Guide to SVMs
Th 10/9 Random Forest Lab 8 - Ensembles and Feature Engineering
  • Lab 7 is due Monday 10/14
  • Lab 8 is due Wednesday 10/16

\(~\)

Week 7 - More Models (cont.)

Date Lecture Lab Resources
T 10/14 Gradient Boosting Lab 8 (cont.)
Th 10/16 Lab 9 - Gradient Boosting with xgboost
  • Lab 9 is due Friday 10/17

\(~\)

Unit #2 - The Mathematics of “Learning”

Week 8 - Gradient Descent Algorithms

Date Lecture Lab Resources
T 10/28 Gradient Descent Lab 10 - Linear Algebra Review and Gradient Descent
Th 10/30 Mini-batch and Stochastic Gradient Descent Finish/discuss Lab 10

\(~\)

Week 9 - Neural Networks and Backpropogation

Date Lecture Lab Resources
T 11/4 Neural Networks
Th 11/6 Lab 11 - Neural Networks

\(~\)

Unit #3 - Introduction to Deep Learning

Coming soon

\(~\)

Final Project

Final Project Description

\(~\)