Useful links
Useful links
Feb 21, 2025·
·
3 min read
Mohsen Davarynejad
Image credit: Unsplash
People
- Prof.dr. Davarynejad
- Zary Forghany
- https://fuzhanrahmanian.com/
- Miriam Hacker, Research Program Manager at The Water Research Foundation
- Giovanni Luca Ciampaglia
- Chloé-Agathe Azencott
- here
Explore post ideas
Explore project ideas
Data sets
Explore course/workshop ideas
- Workshop Machine Learning in R, 2020
- Data science for economists
- Hands-on Machine Learning in Python
Online courses
(I’m not endorsing any of the courses/materials listed below. This is only a place for me to look at them at a later stage!)
Bookshelf - Textbooks available online for free
Optimization and Linear Programming
- Computational Optimal Transport
- Convex Optimization and Euclidean Distance Geometry
- Algorithms for Optimization
- Dynamic Programming
- Model Predictive Control: Theory, Computation, and Design, 2nd Ed.
- Introduction to Online Convex Optimization
- Stochastic Programming, 2nd Ed.
- Linear Programming and Extensions
- Algorithms for Convex Optimization
- Algorithms for Decision Making
- Mathematical Programming
- Convexity and Well-Posed Problems
- Convex Optimization
- Decision Making Under Uncertainty
- The Design of Approximation Algorithms
- Optimization Methods for Large-Scale Machine Learning
- Convex Optimization: Algorithms and Complexity
- Optimization Algorithms on Matrix Manifolds
- Optimal Transport, Old and New
Data Science
- The Fourth Paradigm: Data-Intensive Scientific Discovery
- Python Data Science Handbook
- Computational and Inferential Thinking: The Foundations of Data Science, 2nd Ed.
- A Programmer’s Guide to Data Mining
- Data Science at the Command Line, 2nd Ed.
- Introduction to Data Science
- Foundation of Data Science
- Data Science: Theories, Models, Algorithms and Analytics
- Model-Based Clustering and Classification for Data Science
- Computational Statistics with PyMC3
- Python for Data Analysis, 3rd Ed.
- High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
- Data Assimilation: A Mathematical Introduction
- Mathematics for Computer Science
- Mathematical Foundations of Data Science
- An Introduction to Data Sciences
- Data-Driven Science & Engineering
- Data Science in Context: Foundations, Challenges, Opportunities
- Understanding Random Forests: From Theory to Practice
Machine Learning
- Machine Learning: A First Course for Engineers and Scientists
- A Brief Introduction to Neural Networks
- Machine Learning, Statistics, and Data Mining for Heliophysics
- Foundations of Machine Learning, 2nd Ed.
- Pattern, Predictions, and Actions
- Probabilistic Machine Learning: An Introduction
- The Elements of Statistical Learning, 2nd Ed.
- Interpretable Machine Learning
- Machine Learning (Tom Mitchell)
- A Brief Introduction to Machine Learning for Engineers
- Google Machine Learning Crash Course
- A Course in Machine Learning
- Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond
- Approaching (Almost) Any Machine Learning Problem
- Mathematics for Machine Learning
- Pattern Recognition and Machine Learning
- Boosting: Foundations and Algorithms
- An Introduction to Statistical Learning, 2nd Ed.
- How Smart Machines Think
- Computer Age Statistical Inference
- Bayesian Reasoning and Machine Learning
- Model-Based Machine Learning
- Introduction au Machine Learning
- The LION Way. Machine Learning plus Intelligent Optimization
- The Hundred Pages Machine Learning Book
- Introducing MLOps
