All notes

2026 1

1. 04 Oct 26 The Mistake That Makes Gradient Descent Work Counting to Intelligence

2024 1

1. 18 Apr 24 Data Cleaning for Machine Learning Systems: A Survey Data Cleaning for Machine Learning Sysytems: A Survey Note

2022 79

1. 18 Nov 22 Mixture Models and Expectation Maximization - The EM Algorithm in General Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
2. 04 Nov 22 Mixture Models and Expectation Maximization - An Alternative View of EM Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
3. 18 Oct 22 Mixture Models and Expectation Maximization - Mixtures of Gaussians Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
4. 11 Oct 22 Mixture Models and Expectation Maximization - K-means Clustering Bishop · Chapter 9 — Mixture Models and Expectation Maximization Bishop
5. 04 Oct 22 Graphical Models - The Sum-product Algorithm, The Max-Sum Algorithm Bishop · Chapter 8 — Graphical Models Bishop
6. 20 Sep 22 Graphical Models - Inference in Graphical Models Bishop · Chapter 8 — Graphical Models Bishop
7. 15 Sep 22 Graphical Models - Markov Random Fields Bishop · Chapter 8 — Graphical Models Bishop
8. 11 Sep 22 Graphical Models - Conditional Independence Bishop · Chapter 8 — Graphical Models Bishop
9. 05 Sep 22 Graphical Models - Bayesian Networks Bishop · Chapter 8 — Graphical Models Bishop
10. 28 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers: Relation to Logistic Regression, Multiclass SVMs, SVMs for Regression Bishop · Chapter 7 — Sparse Kernel Methods Bishop
11. 23 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers: Overlapping Class Distributions Bishop · Chapter 7 — Sparse Kernel Methods Bishop
12. 18 Aug 22 Sparse Kernel Methods - Maximum Margin Classifiers Bishop · Chapter 7 — Sparse Kernel Methods Bishop
13. 14 Aug 22 Sparse Kernel Methods - Lagrange Multipliers Bishop · Chapter 7 — Sparse Kernel Methods Bishop
14. 04 Aug 22 Kernel Methods - Gaussian Process Bishop · Chapter 6 — Kernel Methods Bishop
15. 29 Jul 22 Kernel Methods - Constructing Kernels & Radial Basis Function Networks Bishop · Chapter 6 — Kernel Methods Bishop
16. 25 Jul 22 Kernel Methods - Dual Representations Bishop · Chapter 6 — Kernel Methods Bishop
17. 19 Jul 22 Neural Networks - Mixture Density Networks & Bayesian Neural Networks Bishop · Chapter 5 — Neural Networks Bishop
18. 17 Jul 22 Neural Networks - Regularization in Neural Networks Bishop · Chapter 5 — Neural Networks Bishop
19. 15 Jul 22 Neural Networks - The Hessian Matrix Bishop · Chapter 5 — Neural Networks Bishop
20. 13 Jul 22 Neural Networks - Error Backpropagation Bishop · Chapter 5 — Neural Networks Bishop
21. 11 Jul 22 Neural Networks - Network Training Bishop · Chapter 5 — Neural Networks Bishop
22. 09 Jul 22 Neural Networks - Feed-forward Network Functions Bishop · Chapter 5 — Neural Networks Bishop
23. 06 Jul 22 Linear Models for Classification - The Laplace Approximation & Bayesian Logistic Regression Bishop · Chapter 4 — Linear Models for Classification Bishop
24. 05 Jul 22 Linear Models for Classification - Probabilistic Discriminative Models Bishop · Chapter 4 — Linear Models for Classification Bishop
25. 04 Jul 22 Linear Models for Classification - Probabilistic Generative Models (Maximum Likelihood Solution) Bishop · Chapter 4 — Linear Models for Classification Bishop
26. 03 Jul 22 Linear Models for Classification - Probabilistic Generative Models Bishop · Chapter 4 — Linear Models for Classification Bishop
27. 02 Jul 22 Linear Models for Classification - The Perceptron Algorithm Bishop · Chapter 4 — Linear Models for Classification Bishop
28. 01 Jul 22 Linear Models for Classification - Fisher’s Linear Discriminant Bishop · Chapter 4 — Linear Models for Classification Bishop
29. 30 Jun 22 Linear Models for Classification - Least Squares for Classification Bishop · Chapter 4 — Linear Models for Classification Bishop
30. 29 Jun 22 Linear Models for Classification - Discriminant Functions (Part 2) Bishop · Chapter 4 — Linear Models for Classification Bishop
31. 28 Jun 22 Linear Models for Classification - Discriminant Functions Bishop · Chapter 4 — Linear Models for Classification Bishop
32. 27 Jun 22 Linear Models for Regression - Evidence Approximation & Limitations of Fixed Basis Function Bishop · Chapter 3 — Linear Models for Regression Bishop
33. 26 Jun 22 Linear Models for Regression - Bayesian Model Comparison Bishop · Chapter 3 — Linear Models for Regression Bishop
34. 25 Jun 22 Linear Models for Regression - Bayesian Linear Regression Bishop · Chapter 3 — Linear Models for Regression Bishop
35. 23 Jun 22 Linear Models for Regression - Bias-Variance Decomposition Bishop · Chapter 3 — Linear Models for Regression Bishop
36. 21 Jun 22 Linear Models for Regression - Linear Basis Function Models : Part 2 Bishop · Chapter 3 — Linear Models for Regression Bishop
37. 20 Jun 22 Linear Models for Regression - Linear Basis Function Models : Part 1 Bishop · Chapter 3 — Linear Models for Regression Bishop
38. 19 Jun 22 Probability Distributions - Nonparametric Methods Bishop · Chapter 2 — Probability Distributions Bishop
39. 18 Jun 22 Probability Distributions - The Exponential Family Bishop · Chapter 2 — Probability Distributions Bishop
40. 17 Jun 22 Probability Distributions - The Gaussian Distribution: Part 5 Bishop · Chapter 2 — Probability Distributions Bishop
41. 16 Jun 22 Probability Distributions - The Gaussian Distribution: Part 4 Bishop · Chapter 2 — Probability Distributions Bishop
42. 15 Jun 22 Probability Distributions - The Gaussian Distribution: Part 3 Bishop · Chapter 2 — Probability Distributions Bishop
43. 14 Jun 22 Probability Distributions - The Gaussian Distribution: Part 2 Bishop · Chapter 2 — Probability Distributions Bishop
44. 13 Jun 22 Probability Distributions - The Gaussian Distribution: Part 1 Bishop · Chapter 2 — Probability Distributions Bishop
45. 12 Jun 22 Probability Distributions - Multinomial Variables Bishop · Chapter 2 — Probability Distributions Bishop
46. 11 Jun 22 Probability Distributions - Binary Variables Bishop · Chapter 2 — Probability Distributions Bishop
47. 10 Jun 22 Introduction - Information Theory Bishop · Chapter 1 — Introduction Bishop
48. 04 Jun 22 Introduction - Decision Theory Bishop · Chapter 1 — Introduction Bishop
49. 29 May 22 Introduction - Model Selection & Curse of Dimensionality Bishop · Chapter 1 — Introduction Bishop
50. 23 May 22 Introduction - Probability Theory Bishop · Chapter 1 — Introduction Bishop
51. 20 May 22 Introduction - Polynomial Curve Fitting Bishop · Chapter 1 — Introduction Bishop
52. 15 May 22 Left, Right and Pseudo Inverses Strang · Chapter 28 Strang
53. 12 May 22 Linear Transformations, Change of Basis and Image Compression Strang · Chapter 27 Strang
54. 09 May 22 Singular Value Decomposition Strang · Chapter 26 Strang
55. 06 May 22 Similar Matrices Strang · Chapter 25 Strang
56. 02 May 22 Positive Definite Matrices Strang · Chapter 24 Strang
57. 29 Apr 22 Complex Matrices and Fourier Transform Strang · Chapter 23 Strang
58. 26 Apr 22 Symmetric Matrices and Positive Definiteness Strang · Chapter 22 Strang
59. 22 Apr 22 Markov Matrices and Fourier Series Strang · Chapter 21 Strang
60. 20 Apr 22 Differential Equations and Matrix Exponentials Strang · Chapter 20 Strang
61. 17 Apr 22 Diagonalization and Powers of a Matrix Strang · Chapter 19 Strang
62. 14 Apr 22 Eigenvalues and Eigenvectors Strang · Chapter 18 Strang
63. 10 Apr 22 Formula for $A^{-1}$ and Cramer's Rule Strang · Chapter 17 Strang
64. 07 Apr 22 Determinant and Cofactors Strang · Chapter 16 Strang
65. 05 Apr 22 Determinant Strang · Chapter 15 Strang
66. 02 Apr 22 Orthonormal Vectors, Orthogonal Matrices and Gram-Schmidt Method Strang · Chapter 14 Strang
67. 30 Mar 22 Projection Matrices and Least Squares Strang · Chapter 13 Strang
68. 26 Mar 22 Projection of a Matrix Strang · Chapter 12 Strang
69. 23 Mar 22 Orthogonal Vectors and Orthogonal Subspaces Strang · Chapter 11 Strang
70. 21 Mar 22 Graphs, Networks and Incidence Matrices Strang · Chapter 10 Strang
71. 17 Mar 22 Matrix Spaces Strang · Chapter 9 Strang
72. 15 Mar 22 Four Fundamental Subspaces Strang · Chapter 8 Strang
73. 30 Jan 22 Matrix Independence, Span, Basis & Dimension Strang · Chapter 7 Strang
74. 25 Jan 22 Algorithm for solving $Ax=b$ Strang · Chapter 6 Strang
75. 20 Jan 22 Algorithm for solving $Ax=0$ Strang · Chapter 5 Strang
76. 17 Jan 22 Vector Space and Subspace Strang · Chapter 4 Strang
77. 12 Jan 22 Inverse of a Matrix & Factorization into $A=LU$ Strang · Chapter 3 Strang
78. 08 Jan 22 Elimination & Permutation with Matrices Strang · Chapter 2 Strang
79. 03 Jan 22 Geometry of Linear Equations & Matrix Multiplications Strang · Chapter 1 Strang

2018 76

1. 07 Dec 18 The Wilcoxon Signed-Rank Test The Wilcoxon Signed-Rank Test: Derivation of Mean and Variance Note
2. 05 Dec 18 Logistic Regression Logistic Regression: Derivation Note
3. 26 Nov 18 Hypothesis Testing (Part 6) Tests for Variances and Power of a Test Note
4. 25 Nov 18 Hypothesis Testing (Part 5) Tests with Categorical Data & Tests for Homogeneity and Independence Note
5. 25 Nov 18 Hypothesis Testing (Part 4) Distribution-Free Tests Note
6. 24 Nov 18 Hypothesis Testing (Part 3) Tests for the Difference Between Two Means (Large and Small Samples) and Tests with Paired Data Note
7. 23 Nov 18 Hypothesis Testing (Part 2) Tests for a Population Proportion Note
8. 22 Nov 18 Hypothesis Testing (Part 1) Tests for a Population Mean (Large and Small Samples) Note
9. 20 Nov 18 Confidence Intervals (Part 3) Confidence Intervals with Paired Data and Population Variance/ Prediction Intervals Note
10. 18 Nov 18 Confidence Intervals (Part 2) Confidence Intervals for Proportions and the Difference Note
11. 17 Nov 18 Confidence Intervals (Part 1) Confidence Intervals for a Population Mean Note
12. 16 Nov 18 Commonly used Distributions (Part 2) Commonly used Distributions Note
13. 15 Nov 18 Commonly used Distributions (Part 1) Commonly used Distributions Note
14. 14 Nov 18 Measurement and Propagation of Error (Part 2) Measurement and Propagation of Error Note
15. 14 Nov 18 Measurement and Propagation of Error (Part 1) Measurement and Propagation of Error Note
16. 13 Nov 18 Random Variables (Part 3: Jointly Distributed Random Variables) Jointly Distributed Random Variables Note
17. 12 Nov 18 Random Variables (Part 2: Continuous Random Variables) Continuous Random Variables Note
18. 11 Nov 18 Random Variables (Part 1: Discrete Random Variables) Discrete Random Variables Note
19. 29 Oct 18 Performance Metrics for Classification Algorithms Performance Metrics for Classification Algorithms Note
20. 29 Oct 18 Hypothesis testing Hypothesis testing Note
21. 28 Oct 18 Maximum Likelihood Estimation Estimation Note
22. 24 Oct 18 Naive Bayes Classifier Classification Note
23. 10 Sep 18 Correlation Think Stats · Chapter 9 Think Stats
24. 04 Sep 18 Estimation Think Stats · Chapter 8 Think Stats
25. 30 Aug 18 Hypothesis Testing Think Stats · Chapter 7 Think Stats
26. 26 Aug 18 Operations on Distributions Think Stats · Chapter 6 Think Stats
27. 22 Aug 18 Probability Think Stats · Chapter 5 Think Stats
28. 17 Aug 18 Continuous Distributions Think Stats · Chapter 4 Think Stats
29. 12 Aug 18 Cumulative Distribution Functions Think Stats · Chapter 3 Think Stats
30. 08 Aug 18 Descriptive Statistics Think Stats · Chapter 2 Think Stats
31. 05 Aug 18 Statistical Thinking for Programmers Think Stats · Chapter 1 Think Stats
32. 30 Jul 18 Content Based Movie Recommendation Engine Content based recommendation engine Note
33. 19 Jul 18 Unsupervised Learning: Applied Exercises ISLR · Chapter 10 — Unsupervised Learning ISLR
34. 15 Jul 18 Unsupervised Learning: Conceptual Exercises ISLR · Chapter 10 — Unsupervised Learning ISLR
35. 12 Jul 18 Hierarchical Clustering ISLR · Chapter 10 — Unsupervised Learning ISLR
36. 09 Jul 18 K-Means Clustering ISLR · Chapter 10 — Unsupervised Learning ISLR
37. 06 Jul 18 Principal Components Analysis: More on PCA ISLR · Chapter 10 — Unsupervised Learning ISLR
38. 04 Jul 18 Principal Components Analysis ISLR · Chapter 10 — Unsupervised Learning ISLR
39. 29 Jun 18 Support Vector Machines: Applied Exercises ISLR · Chapter 9 — Support Vector Machines ISLR
40. 26 Jun 18 Support Vector Machines: Conceptual Exercises ISLR · Chapter 9 — Support Vector Machines ISLR
41. 23 Jun 18 Support Vector Machines and Kernels ISLR · Chapter 9 — Support Vector Machines ISLR
42. 20 Jun 18 Support Vector Classifiers ISLR · Chapter 9 — Support Vector Machines ISLR
43. 19 Jun 18 Maximal Margin Classifier ISLR · Chapter 9 — Support Vector Machines ISLR
44. 16 Jun 18 Tree-Based Methods: Applied Exercises ISLR · Chapter 8 — Tree-Based Methods ISLR
45. 14 Jun 18 Tree-Based Methods: Conceptual Exercises ISLR · Chapter 8 — Tree-Based Methods ISLR
46. 13 Jun 18 Bagging, Random Forests, Boosting ISLR · Chapter 8 — Tree-Based Methods ISLR
47. 11 Jun 18 Decision Trees ISLR · Chapter 8 — Tree-Based Methods ISLR
48. 08 Jun 18 Moving Beyond Linearity: Applied Exercises ISLR · Chapter 7 — Moving Beyond Linearity ISLR
49. 06 Jun 18 Moving Beyond Linearity: Conceptual Exercises ISLR · Chapter 7 — Moving Beyond Linearity ISLR
50. 04 Jun 18 Local Regression, Generalized Additive Models ISLR · Chapter 7 — Moving Beyond Linearity ISLR
51. 30 May 18 Smoothing Splines ISLR · Chapter 7 — Moving Beyond Linearity ISLR
52. 28 May 18 Regression Splines ISLR · Chapter 7 — Moving Beyond Linearity ISLR
53. 28 May 18 Polynomial Regression, Step Functions, Basis Functions ISLR · Chapter 7 — Moving Beyond Linearity ISLR
54. 26 May 18 Linear Model Selection and Regularization: Applied Exercises ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
55. 25 May 18 Linear Model Selection and Regularization: Conceptual Exercises ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
56. 24 May 18 Dimension Reduction Methods ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
57. 22 May 18 Shrinkage Methods ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
58. 21 May 18 Subset Selection ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
59. 19 May 18 Resampling Methods: Applied Exercises ISLR · Chapter 5 — Resampling Methods ISLR
60. 18 May 18 Resampling Methods: Conceptual Exercises ISLR · Chapter 5 — Resampling Methods ISLR
61. 18 May 18 The Bootstrap ISLR · Chapter 5 — Resampling Methods ISLR
62. 17 May 18 Cross-Validation ISLR · Chapter 5 — Resampling Methods ISLR
63. 16 May 18 Classification: Applied Exercises ISLR · Chapter 4 — Classification ISLR
64. 15 May 18 Classification: Conceptual Exercises ISLR · Chapter 4 — Classification ISLR
65. 14 May 18 Linear Discriminant Analysis ISLR · Chapter 4 — Classification ISLR
66. 13 May 18 Logistic Regression ISLR · Chapter 4 — Classification ISLR
67. 11 May 18 Linear Regression: Applied Exercises ISLR · Chapter 3 — Linear Regression ISLR
68. 10 May 18 Linear Regression: Conceptual Exercises ISLR · Chapter 3 — Linear Regression ISLR
69. 09 May 18 Other Considerations in the Regression Model ISLR · Chapter 3 — Linear Regression ISLR
70. 08 May 18 Multiple Linear Regression ISLR · Chapter 3 — Linear Regression ISLR
71. 08 May 18 Simple Linear Regression ISLR · Chapter 3 — Linear Regression ISLR
72. 06 May 18 Statistical Learning: Applied Exercises ISLR · Chapter 2 — Statistical Learning ISLR
73. 06 May 18 Statistical Learning: Conceptual Exercises ISLR · Chapter 2 — Statistical Learning ISLR
74. 05 May 18 Assessing Model Accuracy ISLR · Chapter 2 — Statistical Learning ISLR
75. 04 May 18 What Is Statistical Learning? ISLR · Chapter 2 — Statistical Learning ISLR
76. 02 May 18 Introduction to Statistical Learning ISLR · Chapter 1 — Introduction ISLR