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
