Appendices
20
Appendix 3: Understanding Shapley Values from SHAP
Introduction to Applied Machine Learning
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Course Syllabus
1
Overview of Machine Learning
2
Exploratory Data Analysis
3
Introduction to Regression Models
4
Introduction to Classification Models
5
Resampling Methods for Model Selection and Evaluation
6
Regularization and Penalized Models
7
Midterm Exam
8
Advanced Performance Metrics
9
Advanced Models: Decision Trees, Bagging Trees, and Random Forest
10
Advanced Models: Neural Networks
11
Explanatory Approaches
12
Natural Language Processing: Text Processing and Feature Engineering
13
Applications for Machine Learning: Synthesis and Concept Generalization
14
Ethical Issues in Machine Learning Research and Applications
15
Final Exam and Project
Appendices
16
Key Terminology and Concepts
17
Novel Levels in Held-Out Set(s)
18
Installing Keras for Neural Networks
19
Appendix 2: Simulations of Model Performance Bias and Variance by Resampling Techniques
20
Appendix 3: Understanding Shapley Values from SHAP
21
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals
22
Test
Appendices
20
Appendix 3: Understanding Shapley Values from SHAP
20
Appendix 3: Understanding Shapley Values from SHAP
19
Appendix 2: Simulations of Model Performance Bias and Variance by Resampling Techniques
21
Appendix 4: Bootstrapping Standard Errors & Confidence Intervals