Lecture Slides
5 decks · download- L0 · Introduction to MLAWS MLU — lecture slides · .pptx · 11 MBPPTXDownload
- L1 · Jupyter and SageMakerAWS MLU — lecture slides · .pptx · 2 MBPPTXDownload
- L2 · Types of ML & Successful ML ProblemsAWS MLU — lecture slides · .pptx · 5 MBPPTXDownload
- L3 · Examples of ML ApplicationsAWS MLU — lecture slides · .pptx · 2 MBPPTXDownload
- L4 · ML Lifecycle, Over/UnderfittingAWS MLU — lecture slides · .pptx · 2 MBPPTXDownload
Reading Material — ML Foundations
6 pages- 1.0 Module Overview
- 1.1 Welcome & Course Roadmap
- 1.2 What Is ML, AI, and Deep Learning?
- 1.3 Traditional Programming vs. Machine Learning
- 1.4 Your First Model: y = mx + b
- 1.5 Correlation: The Foundation of ML
Assignments
7 items · 570 pts- Assignment 1.1: Introduction to AI + Setup Anaconda50 pts · due Jun 23, 2026, 3:59 AM50 pts
- Assignment 1.2: Teachable Machine Exercise20 pts · due Jun 24, 2026, 3:59 AM20 pts
- Assignment 1.3: AutoGluon Lab Questions100 pts · due Jun 24, 2026, 3:59 AM100 pts
- Assignment 1.4: Do Titanic Demo using Orange100 pts · due Jun 24, 2026, 10:00 PM100 pts
- Assignment 1.5: Generalization and Overfitting100 pts · due Jun 26, 2026, 3:59 AM100 pts
- Assignment 1.6: Extending Titanic Modeling with Orange150 pts · due Jun 27, 2026, 3:59 AM150 pts
- Assignment 1.7: Unsupervised Learning (Clustering, k-means, Iris Dataset)50 pts · due Jun 28, 2026, 3:59 AM50 pts