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Curriculum

  • 12 Sections
  • 72 Lessons
  • Lifetime
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  • Survival Analysis
    11
    • 1.1
      Notes (PDF)
    • 1.2
      Intro to Survival Analysis
    • 1.3
      Simple ML Example
    • 1.4
      Survival function role in Maximum Likelihood
    • 1.5
      Kaplan Meier (KM) & Nelson Aalen (NA)
    • 1.6
      Mean & Restricted Mean vs. Median
    • 1.7
      Cox Proportional Hazard
    • 1.8
      Deriving the Partial Likelihood
    • 1.9
      Breslow Estimator for the Baseline Hazard
    • 1.10
      Accelerated Failure Time (AFT)
    • 1.11
      AFT vs. CoxPH
  • Expectation Maximization (EM)
    5
    • 2.1
      Notes (PDF)
    • 2.2
      EM Algorithm
    • 2.3
      EM on Binomial Mixture Models
    • 2.4
      EM on Gaussian Mixture Models (GMM)
    • 2.5
      GMM solved by Maximum Likelihood
  • Multiple Hypothesis Testing
    7
    • 3.1
      Notes (PDF)
    • 3.2
      FWER & Bonferroni
    • 3.3
      FDR & Benjamini Hochberg
    • 3.4
      Proof of Benjamini Hochberg
    • 3.5
      Example – Code in R
    • 3.6
      Knockoffs – Original Paper (fixed X)
    • 3.7
      Knockoffs – Construction
  • Computational Statistics
    11
    • 4.1
      Gauss Newton – Non Linear Least Squares
    • 4.2
      Riemann Sum, Rejection Sampling, Importance Sampling – Part 1
    • 4.3
      Riemann Sum, Rejection Sampling, Importance Sampling – Part 2
    • 4.4
      Rejection Sampling – Bounding Constant
    • 4.5
      Rejection Sampling – Proof
    • 4.6
      Sampling Importance Resampling (SIR)
    • 4.7
      Profile Likelihood
    • 4.8
      Profile Likelihood – what is a profile?
    • 4.9
      Profile Likelihood – simpler examples
    • 4.10
      Laplace’s Method
    • 4.11
      Random Sampling – Uniform & Inverse Transform
  • Gaussian Process Regression
    5
    • 5.1
      Notes (PDF)
    • 5.2
      Gaussian Process Regression- part 1 – Kernel First
    • 5.3
      Gaussian Process Regression- part 2 – Prior view
    • 5.4
      Gaussian Process Regression- part 3 – Linear Estimator
    • 5.5
      Gaussian Process Regression- part 4 – Code
  • Other
    5
    • 6.1
      Jacobian
    • 6.2
      Dirac Delta
    • 6.3
      What is probability?
    • 6.4
      Mahalanobis Distance
    • 6.5
      R vs. Python – Coding Differences
  • Factor Analysis
    9
    • 7.1
      Material
    • 7.2
      (Exploratory) Factor Analysis – Introduction
    • 7.3
      (Exploratory) Factor Analysis – Estimation
    • 7.4
      (Exploratory) Factor Analysis – Rotation
    • 7.5
      (Exploratory) Factor Analysis – Code in R
    • 7.6
      Exploratory vs. Confirmatory Factor Analysis
    • 7.7
      (Confirmatory) Factor Analysis – Code in R
    • 7.8
      SEM – Structural Equations Modelling
    • 7.9
      SEM – Code in R
  • Time Series Analysis
    7
    • 8.1
      TSA – Classical Decomposition
    • 8.2
      TSA – Baseline Forecasting
    • 8.3
      TSA – Residuals and Prediction Intervals
    • 8.4
      TSA – Simple Exponential Smoothing – SES
    • 8.5
      TSA – Exponential Smoothing – Trend and/or Seasonality (Holt Winters)
    • 8.6
      ARIMA – Introduction
    • 8.7
      ARIMA – Exploration
  • Quantile Regression
    3
    • 9.1
      Linear vs. Quantile Regression
    • 9.2
      Quantile Loss
    • 9.3
      Numerical Solutions
  • Clustering
    1
    • 10.1
      Hopkins Statistic
  • Copulas
    2
    • 11.2
      Copulas 1: A Gentle Introduction
    • 11.3
      Copulas 2: A Full Hands-On Deep Dive in R
  • MCMC
    6
    • 12.1
      Slides
    • 12.2
      Introduction
    • 12.3
      Markov Chains
    • 12.4
      Random Walk MCMC
    • 12.5
      Gibbs Sampling
    • 12.6
      Hamiltonian Monte Carlo
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Notes (PDF)
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FDR & Benjamini Hochberg
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