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  • Writing

    • Writing
    • Opinions and Speculations
      • A View into Government Cybersecurity
      • Good Data Science is Mostly Dispatch
    • Probabilistic Graphical Models
      • Part 1: An Introduction to Probabilistic Graphical Models
      • Part 2: Exact Inference
      • Part 3: Variational Inference
      • Part 4: Monte Carlo Methods
      • Part 5: Learning Parameters of a Bayesian Network
      • Part 6: Learning Parameters of a Markov Network
      • Part 7: Structure Learning
      • Summaries
      • Notation Guide
    • Machine Learning and Other Topics
      • When does the Delta Method approximation work?
      • Bias-Variance Trade-Off
      • Information Theory and Entropy
      • Generalized Linear Models
      • Jensen's Inequality
      • The Fisher Information
      • Bayesian Optimization
      • The Exponential Family
      • Motivating the Gini Impurity Metric
      • A Brief Explanation and Application of Gaussian Processes
      • The Trace as a Measure of Complexity
    • Linear Algebra
      • Randomized Numerical Linear Algebra
      • The Matrix Inversion Lemma
      • Singular Value Decomposition and the Fundamental Theorem of Linear Algebra
      • The Conjugate Gradient Method
    • Financial Engineering
      • The Copula and 2008
      • The Fundamental Law of Active Management
    • Reinforcement Learning
      • A Reliable Contextual Bandit Algorithm: LinUCB
      • Contextual Bandits as Supervised Learning
      • Reinforcement Learning at Lyft
    • Recommender Systems
      • How to Understand Recommender Systems
      • Evaluating Recommender Systems

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