Computational X Repository

Mathematics
Financial Modeling and Econometrics
Time Series
Modeling Process
Spatial Data
R
Model Explainability & Interpretability
General
Natural Language Processing
PyTorch
Machine Learning
1. Semi-Supervised Learning

Introduction

The following is a repository of direct links to Computer Science, Statistics, Machine Learning, Data Mining, Artificial Intelligence Books, and the like books, as I come around them. These will be mostly from the Open Publishing/ Freely made available content domain. If you find a book that does not belong here or should be removed, please raise the issue.

Mathematics

S. M. Ross: Introduction to Probability Models 10th Edition
https://fac.ksu.edu.sa/sites/default/files/introduction-to-probability-model-s.ross-math-cs.blog_.ir_.pdf

E. Lehman and T. Leighton: Mathematics for Computer Science (2004 Ed.)
https://www.cs.princeton.edu/courses/archive/fall06/cos341/handouts/mathcs.pdf

M. P. Deisenroth, A. A. Faisal, and C. S. Ong: Mathematics for Machine Learning (2020 Ed.)
https://mml-book.github.io/

Financial Modeling and Econometrics

B. Pfaff: Financial Risk Modelling and Portfolio Optimization with R (2nd Ed.)
https://englianhu.files.wordpress.com/2017/09/financial-risk-modelling-and-portfolio-optimization-with-r-2nd-edt.pdf

T. Carilli: R Companion to Real Econometrics (2019 Ed.)
https://bookdown.org/carillitony/bailey/

Time Series

E. E. Holmes, M. D. Scheuerell, and E. J. Ward: Applied Time Series Analysis for Fisheries and Environmental Sciences
https://nwfsc-timeseries.github.io/atsa-labs/

R. J. Hyndman, and G. Athanasopoulos: Forecasting: Principles and Practice (2018 Ed.)
https://otexts.com/fpp2/

R. H. Shumway, and D. S. Stoffer: Time Series Analysis and Its Applications With R Examples (4th Edition)
https://www.stat.pitt.edu/stoffer/tsa4/tsa4.pdf

M. Falk: A First Course onTime Series AnalysisExamples with SAS (2011)
https://www.uni-wuerzburg.de/fileadmin/10040800/user_upload/time_series/the_book/2011-March-01-times.pdf

Modeling Process

M. Kuhn and K. Johnson: Feature Engineering and Selection: A Practical Approach for Predictive Models (2019 Ed.)
https://bookdown.org/max/FES/

Spatial Data

E. Pebesma, and R. Bivand: Spatial Data Science (2020 Ed.)
https://keen-swartz-3146c4.netlify.app/

R

Language

P. Burns: The R Inferno
https://www.burns-stat.com/documents/books/the-r-inferno/

B. Rodrigues: Functional programming and unit testing for data munging with R (2017 Ed.
https://b-rodrigues.github.io/fput/

Package and Ecosystem

B. Rodrigues: Modern R with the tidyverse (2020 Ed.)
https://b-rodrigues.github.io/modern_R/

H. Wickham - R Packages: Organize, Test, Document, and Share Your Code (1st Ed.)
http://r-pkgs.had.co.nz/

Data Science

G. Grolemund, and H. Wickham: R for Data Science
https://r4ds.had.co.nz/

R. A. Irizarry: Introduction to Data Science - Data Analysis and Prediction Algorithms with R (2020 Ed.)
https://rafalab.github.io/dsbook/

Model Explainability & Interpretability

C. Molnar: Interpretable Machine Learning - A Guide for Making Black Box Models Explainable (2020 Ed.)
https://christophm.github.io/interpretable-ml-book/

C. Ismay and A. Y. Kim: Statistical Inference via Data Science - A ModernDive into R and the tidyverse (2019 Ed.)
https://moderndive.com/index.html

P. Biecek and T. Burzykowski: Explanatory Model Analysis - Explore, Explain and Examine Predictive Models (2020 Ed.)
https://pbiecek.github.io/ema/

General

A. Reinhart: Statistics Done Wrong - The woefully complete guide
https://www.statisticsdonewrong.com/

Y. Xie, A. Thomas, A. Presmanes Hill: blogdown: Creating Websites with R Markdown (2020 Ed.)
https://bookdown.org/yihui/blogdown/

B. Caffo: Statistical inference for data science
https://leanpub.com/LittleInferenceBook

B. Caffo: Regression Models for Data Science in R
https://leanpub.com/regmods

B. Caffo: Advanced Linear Models for Data Science
https://leanpub.com/lm

Natural Language Processing

A. Clark, C. Fox, and S. Laplin: The Handbook of Computational Linguistics and Natural Language Processing (2010)
http://course.duruofei.com/wp-content/uploads/2015/05/Clark_Computational-Linguistics-and-Natrual-Language-Processing.pdf

J. Eisenstein: Natural Language Processing (2018 Ed.) https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf

PyTorch

Unkown: Torch book (2020 Ed.)
https://mlverse.github.io/torchbook/

Machine Learning

Semi-Supervised Learning

O. Chapelle, B. Scholkopf, A. Zien: Semi-Supervised Learning (2006 Ed.)
https://www.molgen.mpg.de/3659531/MITPress--SemiSupervised-Learning.pdf

christianbitter/techbooks