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- 1711.02582 (
May 26, 2025
)
6.041SC Probabilistic Systems Analysis and Applied Probability by
John N. Tsitsiklis
- Lecture 5 + 6 + 7: Discrete Random Variables (
Mar 31, 2025
)
- Lecture 5 + 6 + 7: Discrete Random Variables - Examples and Exercises (
Mar 31, 2025
)
- Lecture 8: Continuous Random Variables (
Mar 17, 2025
)
- Lecture 9 + 10: Joint Distribution, Conditioning and Independence (
Apr 19, 2025
)
6.042J: Mathematics for Computer Science by
Tomodachii
- Lecture 6: Graph Theory and Coloring (
Jun 2, 2025
)
Causality by
Judea Pearl
- Chapter 1: Introduction to Probabilities, Graphs and Causal Models (
May 22, 2025
)
CS109: Probability for Computer Scientists by
Chris Piech
- Lecture 1 Counting (
Apr 14, 2025
)
- Lecture 21: MLE (
Apr 7, 2025
)
Density Estimation for Statistics and Data Analysis by
B.W. Silverman
- Master Note (
Apr 4, 2025
)
- Chapter 1 + 2: Introduction and Survey of existing methods (
Apr 3, 2025
)
- Chapter 3: The kernel method for univariate data (
Apr 3, 2025
)
EE364a: Convex Optimization I by
Stephen P. Boyd
- Lecture 1 Introduction (
May 23, 2025
)
Introduction to Causal Inference from a Machine Learning Perspective by
Brady Neal
- Lecture 1: Motivation (
Apr 16, 2025
)
- Lecture 2: Potential Outcomes (
May 21, 2025
)
- Lecture 3: The Flow of Association and Causation in Graphs (
Jun 3, 2025
)
Probabilistic Graphical Models by
Daphne Koller
,
Nir Friedman
- Chapter 1 + 2 and Appendix : Introduction, Foundations and Background Material (
Mar 19, 2025
)
- Chapter 3: The Bayesian Network Representation (
Mar 19, 2025
)
- Chapter 4 Undirected Graphical Models (
Apr 9, 2025
)
- Chapter 5: Local Probabilistic Models (
Mar 28, 2025
)
- Chapter 8 the Exponential Family (
Apr 26, 2025
)
- Chapter 9 (Pt. 1): Exact Inference - Queries (
Apr 3, 2025
)
- Chapter 9 (Pt. 2): Exact Inference - Variable Elimination (
Apr 3, 2025
)
- Chapter 10 Exact Inference Clique Trees (
Apr 18, 2025
)
- Chapter 11 (Pt. 1): Inference as Optimization (
May 28, 2025
)
- Chapter 11 (Pt. 2): Loopy BP (
May 4, 2025
)
- Chapter 11 (Pt. 3): Mean Field Approximation (
May 28, 2025
)
- Chapter 12: Particle-Based Approximate Inference (
Jun 4, 2025
)
- Chapter 16: Learning Graphical Models - Overview (
Apr 15, 2025
)
- Chapter 17: Parameter Estimation (
Apr 15, 2025
)
Probabilistic Machine Learning: An Introduction by
Kevin P. Murphy
- Chapter 2: Probability - Univariate Models (
Apr 6, 2025
)
- Chapter 8: Optimization (
May 23, 2025
)
Random by
Tomodachii
- Insertion Sort (
Jul 24, 2024
)