Office Hours: Email any time

• Overview of this Course
• Overview of Machine Learning
• Evaluating 101: FP/FN and Confusion Matrices
• Logistic Regression

• Mitchell ch 1
• Hulten ch 1-3
• Hulten ch 19 (pp 225 - 229)

• Feature Engineering (Text focus)
• Evaluating Models 201: Error bounds and cross validation

• Mitchell ch 5
• Hulten ch 11, 12, 16, 17

• Implementing Machine Learning Systems
• ROC curves and operating points
• Decision Trees

• Mitchell ch 3
• Hulten ch 19 (finish it)

• Overfitting and Underfitting
• Ensembles & Combining Intelligence
• Machine Learning with an Abuser

• Mitchell (optional) ch 7
• Sections 1,3, & 4 of Original Random Forest Paper
• Hulten ch 20, 21, 25

• Approaching a ML Problem
• Basics of Computer Vision
• Clustering and Instance Based Learning

• Mitchell 8
• Hulten ch 4, 18 (for Approaching a ML Problem)

• Intelligence Management & Online Evaluation
• Neural Networks

• Mitchell 4
• Hulten ch 13,14,15 (for Intelligence Management)
• Hulten ch 5,6,7 (for Intelligent User Experiences)

• Catch up on assignments...

• Watch Gradient Boosting Video

• Introduction to Intelligent User Experiences
• Neural Network Architectures
• Neural Network Architectures Assignment Support

• Hulten ch 8,9,10
• ResNet Paper

• Orchestrating an Intelligent System
• Reinforcement Learning

• Mitchell ch 13
• Hulten ch 22,23,24
• Watch AlphaZero Talk

• Other Important Machine Learning
• Review of the Course

• Mitchell ch 6 (optional)
• Hulten ch 26