Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields.
Key Features:
- The book is structured for those who have been trained in a traditional statistics curriculum.
- There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis.
- The book develops a background framework for Machine learning applications from Nonparametric methods.
- SVM and NN simple enough without too much detail. Its self-sufficient.
- Nonparametric time-series predictions are new and covered in a separate section.
- Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields.
1. How We Define Machine Learning
2. Preliminaries Part
1. Formal Look
at Prediction
3. Bias-Variance Tradeoff
4. Overfitting Part
2. Nonparametric
Estimations
5. Parametric Estimations
6. Nonparametric Estimations - Basics
7. Smoothing
8. Nonparametric Classifier - kNN Part
3. Self-learning
9.
Hyperparameter Tuning
10. Tuning in Classification
11. Classification Example
Part
4. Tree-based Models
12. CART
13. Ensemble Learning
14. Ensemble
Applications Part
5. SVM & Neural Networks
15. Support Vector Machines
16.
Artificial Neural Networks Part
6. Penalized Regressions
17. Ridge
18. Lasso
19. Adaptive Lasso
20. Sparsity Part
7. Time Series Forecasting
21. ARIMA
models
22. Grid Search for Arima
23. Time Series Embedding
24. Random Forest
with Times Series
25. Recurrent Neural Networks Part
8. Dimension Reduction
Methods
26. Eigenvectors and eigenvalues
27. Singular Value Decomposition
28.
Rank r approximations
29. Moore-Penrose Inverse
30. Principle Component
Analysis
31. Factor Analysis Part
9. Network Analysis
32. Fundamentals
33.
Regularized Covariance Matrix Part
10. R Labs
34. R Lab 1 Basics
35. R Lab 2
Basics II
36. Simulations in R
37. Algorithmic Optimization
38. Imbalanced
Data
Yigit Aydede is a Sobey Professor of Economics at Saint Marys University, Halifax, Nova Scotia, Canada. He is a founder member of the Research Portal on Machine Learning for Social and Health Policy, a joint initiative by a group of researchers from Saint Marys and Dalhousie universities
