https://github.com/gstechschulte/autodidact

Applied Statistics, Linear Algebra, Science and Technology through trial and error in programming
https://github.com/gstechschulte/autodidact

linear-algebra machine-learning probability statistics

Last synced: 24 days ago
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Applied Statistics, Linear Algebra, Science and Technology through trial and error in programming

• Host: GitHub
• URL: https://github.com/gstechschulte/autodidact
• Owner: GStechschulte
• Created: 2021-05-19T09:02:40.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-24T20:05:46.000Z (almost 2 years ago)
• Last Synced: 2024-04-23T13:52:54.805Z (7 months ago)
• Topics: linear-algebra, machine-learning, probability, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 33.9 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Self Learning

A place for programming and experimenting with various methods and algorithms.

### Probability and Statistics

 - [Central Limit Theorem](statistics/central_limit_theorem.ipynb)

 - [Correlation](statistics/correlation.ipynb)

 - [Law of Large Numbers](statistics/law_of_large_numbers.ipynb)

 - [MLE and MAP](statistics/mle_map.ipynb)

 - [Epidemiology](statistics/epidemiology.ipynb)

 - [KL Divergence](probability/kl_divergence.ipynb)

 - [Fundamental Theorem of Calculus](probability/fundamental_theorem_calculus.ipynb)

 - [MCMC First principles](probability/mcmc.ipynb)

 - [Poisson Distribution](statistics/poisson_distribution.ipynb)

 - [Frequentist Uncertainty](statistics/frequentist_uncertainty.ipynb)

 - [Epidemiology](statistics/epidemiology.ipynb)

 - [Variational Inference - ELBO](probability/vi_elbo.py)

 - [MCMC Approximation](probability/monte_carlo_approximation.py)

 - [Metropolis-Hastings](probability/samplers/samplers_metroplis_hasting_v2.py)

 - [Gibbs sampling a 2d GMM](probability/samplers/samplers_gibbs_2d_gmm.py)

 - [Hamiltonian Monte Carlo](probability/samplers/samplers_hmc.py)

### Linear Algebra

 - [Cross, Inner, Outer, and Dot Products](linear_algebra/Cross-Inner-Outer-Products.ipynb)

 - [Linear Combinations, Spans, and Basis Vectors](linear_algebra/Linear-Combinations-Span-Basis.ipynb)

 - [Solving systems of equations with Inverse Matrices, Rank, and Null Space](Inverse-Column-Null-Space.ipynb)

 - [Matrix Multiplication as a Composition](linear_algebra/Matrix-Multiplication-Transformations.ipynb)

 - [The Determinant](linear_algebra/Determinant.ipynb)

 - [3d Linear Transformations](linear_algebra/3d-Linear-Transformations.ipynb)

 - [Abstract Vector Spaces](linear_algebra/Abstract-Vector-Spaces.ipynb)

 - [Eigenvectors and Eigenvalues](linear_algebra/Eigenvectors-Eigenvalues.ipynb)

 - [Singular Value Decomposition](linear_algebra/SVD.ipynb)

### Machine Learning from Scratch

 - [Logistic Regression](vanilla_ml/logistic_regression.ipynb)

 - [Generative Classifer](vanilla_ml/kde.ipynb)

 - [Gradient Descent](vanilla_ml/gradient_descent.ipynb)

 - [Kernel Functions](vanilla_ml/kernel_functions.ipynb)

 - [Gaussian Processes](vanilla_ml/gaussian_processes.ipynb)

 - [Inference vs. Prediction pt.1 & 2](vanilla_ml/inference_vs_prediction_pt1.ipynb)

 - [Making a Algorithm Learn](vanilla_ml/learning_and_nn.ipynb)

 - [Principal Component Analysis](vanilla_ml/pca.py)

### Microeconomics

- [Bayesian Demand Uncertainty and Forecasting](microeconomics/bayesian_demand_uncertainty.ipynb)

- [Ride Hailing Services Regression Discontinuity](microeconomics/regression-discontinuity/)

- [Marginal cost curves](microeconomics/marginal.ipynb)