Machine Learning for Statistical Inference
( 30 Modules )

Module #1
Introduction to Machine Learning for Statistical Inference
Overview of the course, importance of machine learning in statistical inference, and course objectives

Module #2
Review of Statistical Inference
Refresher on statistical inference concepts, including hypothesis testing, confidence intervals, and p-values

Module #3
Introduction to Machine Learning
Basics of machine learning, including supervised and unsupervised learning, types of machine learning algorithms, and evaluation metrics

Module #4
Regression Analysis in Machine Learning
Regression analysis in machine learning, including simple and multiple regression, and regularization techniques

Module #5
Classification in Machine Learning
Classification in machine learning, including logistic regression, decision trees, and random forests

Module #6
Model Selection and Hyperparameter Tuning
Techniques for model selection and hyperparameter tuning, including cross-validation and grid search

Module #7
Introduction to Bayesian Machine Learning
Basics of Bayesian machine learning, including Bayes theorem, prior and posterior distributions, and MCMC methods

Module #8
Bayesian Linear Regression
Bayesian approach to linear regression, including Bayesian linear model, conjugate priors, and Gibbs sampling

Module #9
Bayesian Generalized Linear Models
Bayesian approach to generalized linear models, including logistic regression, Poisson regression, and log-linear models

Module #10
Machine Learning for Causal Inference
Introduction to causal inference, including causal graphs, confounding variables, and instrumental variables

Module #11
Causal Trees and Forests
Causal trees and forests, including causal structure learning and causal effect estimation

Module #12
Instrumental Variable Regression
Instrumental variable regression, including two-stage least squares and generalized method of moments

Module #13
Machine Learning for Time Series Analysis
Introduction to time series analysis, including stationary and non-stationary processes, and ARIMA models

Module #14
Machine Learning for Time Series Forecasting
Machine learning methods for time series forecasting, including recurrent neural networks and long short-term memory networks

Module #15
Survival Analysis with Machine Learning
Introduction to survival analysis, including Kaplan-Meier estimation and Cox proportional hazards model

Module #16
Machine Learning for Survival Analysis
Machine learning methods for survival analysis, including random survival forests and deep survival networks

Module #17
Big Data and Machine Learning for Statistical Inference
Challenges and opportunities of big data for statistical inference, including scalable machine learning algorithms and distributed computing

Module #18
Case Studies in Machine Learning for Statistical Inference
Real-world applications of machine learning for statistical inference, including examples from genetics, finance, and healthcare

Module #19
Assumptions and Limitations of Machine Learning for Statistical Inference
Discussion of assumptions and limitations of machine learning methods for statistical inference, including bias, variance, and overfitting

Module #20
Interpretable Machine Learning for Statistical Inference
Techniques for interpretable machine learning, including feature importance, partial dependence plots, and SHAP values

Module #21
Uncertainty Quantification in Machine Learning for Statistical Inference
Methods for uncertainty quantification in machine learning, including Bayesian neural networks and bootstrap methods

Module #22
Machine Learning for Missing Data Imputation
Machine learning methods for missing data imputation, including multiple imputation and generative adversarial networks

Module #23
Machine Learning for Data Integration
Machine learning methods for data integration, including data fusion and transfer learning

Module #24
Machine Learning for Healthcare and Biomedical Applications
Applications of machine learning for statistical inference in healthcare and biomedical research, including personalized medicine and genomic data analysis

Module #25
Machine Learning for Finance and Economics Applications
Applications of machine learning for statistical inference in finance and economics, including risk modeling and portfolio optimization

Module #26
Machine Learning for Environmental and Climate Applications
Applications of machine learning for statistical inference in environmental and climate research, including climate modeling and air quality forecasting

Module #27
Machine Learning for Social Sciences Applications
Applications of machine learning for statistical inference in social sciences, including social network analysis and survey research

Module #28
Machine Learning for Computer Vision and Imaging Applications
Applications of machine learning for statistical inference in computer vision and imaging, including image classification and object detection

Module #29
Machine Learning for Natural Language Processing Applications
Applications of machine learning for statistical inference in natural language processing, including text classification and sentiment analysis

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Statistical Inference career

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Machine Learning for Statistical Inference ( 30 Modules )

Machine Learning for Statistical Inference
( 30 Modules )