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Statistical Inference
Readings
Reading 1: Probability and Random Variables
Reading 2: Summary Statistics
Reading 3: Classical Inference I: Maximum Likelihood Estimation
Reading 4: Classical Inference II: Estimating and Comparing Distributions
Reading 5: Monte Carlo Method and Importance Sampling
Reading 6: Stochastic Processes
Reading 7: The Ising Model
Reading 8: MCMC Algorithms and Considerations
Reading 9: Bayesian Analysis
Reading 10: MCMC Inference
Labs
Lab 0: Python Recap
Lab 1: Sampling Random Variables
Lab 2: Summary Statistics
Lab 3: Classical Inference I: Maximum Likelihood Estimation
Lab 4: Classical Inference II: Estimating and Comparing Distributions
Lab 5: Importance Sampling
Lab 6: Random walk and Brownian motion
Lab 7: Ising Model
Lab 8: MCMC Algorithms and Considerations
Lab 9: Bayesian Analysis
Lab 10: MCMC Inference
Machine Learning
Readings
Reading 11: Introduction to Machine Learning
Reading 12: Regression I: Introduction, Linear Models, Regularization
Reading 13: Regression II: Gaussian Process, Cross Validation
Reading 14: Classification I: Introduction, Generative Classification
Reading 15: Classification II: Discriminative Classification, Ensemble Methods
Reading 16: Classification III: Neural Networks, Evaluating Classifiers
Reading 17: Density Estimation and Clustering
Reading 18: Dimensionality Reduction
Labs
Lab 11: Introduction to scikit-learn
Lab 12: Regression I: Linear Models
Lab 13: Regression II: Gaussian Process and Cross Validation
Lab 14: Classification I: Prediction and Probability Calibration
Lab 15: Classification II: Decision Trees and Ensemble Methods
Lab 16: Classification III: Neural Networks and Deep Learning
Lab 17: Density Estimation and Clustering
Lab 18: Dimensionality Reduction
Index
