Weekly Course Schedule – Fall 2024

IS597 - MLC - Machine Learning Pipelines Using Cloud-Based Platforms

Meets Tuesday Afternoons

Week 10 (October 28 – November 3)



Tuesday, October 29   (1:15 P.M. – 3:15 P.M., LISB 131)
Class Session 10
Topics
  • Dimensionality Reduction
  • Model Tuning
In-Class Activities
  • Review solutions to prior assignments from prior week.
  • Lecture: Dimensionality Reduction
  • Lecture: Model Tuning
  • Breakout Activity: TBA
Required Readings
  • Géron – Chapter 8 (Dimensionality Reduction)
  • Raschka & Mirjalili – Chapter 5 (Compressing Data via Dimensionality Reduction)
  • Raschka & Mirjalili – Chapter 6 (Learning Best Practices for Model Evaluation and Hyperparameter Tuning)
  • Raschka & Mirjalili – Chapter 7 (Combining Different Models for Ensemble Learning)
Required Recordings
Other Resources


Friday, November 1   (2:00 P.M. – 3:00 P.M., Zoom)
Optional Session
Lab Session
On Friday afternoons from 2:00 PM till 3:00 PM, we will be holding an optional Lab Session using Zoom. Please drop by to ask a question, to discuss solutions to previous assignments, to get help with the current assignment, to discuss the final project, or just to say hello. Please use a headset while participating.


Sunday, November 3, @  11:55 P.M.
Weekly Assignments Deadline
Coding Assignments Due
  • Dimensionality Reduction & Model Tuning Assignment
    Description: In this assignment, you will implement dimensionality reduction and tune your models. In this assignment, you will be expected to create more than one Jupyter Notebook. You will be running your notebook(s) in an AWSALL SageMaker Notebook Instance. Later, you will be able to adopt this approach for use in your Final Project.
    • Dimensionality Reduction & Model Tuning Assignment Instructions and Starter Files: Resource is not yet available. Expected availability date is Monday, October 28.
    • Dimensionality Reduction & Model Tuning Assignment Grading Rubric: Resource is not yet available. Expected availability date is Monday, October 28.