21
References
Data Science Tools for Classical Operations Research
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1
Modern Operations Research with Python
Part I: Foundations
2
Linear Algebra Foundations for Operations Research and Machine Learning
3
Probability and Statistics Foundations for OR and ML
4
Mathematical Modeling Principles for Operations Research and Machine Learning
Part II: Classical Optimization
5
Introduction to Operations Research and Machine Learning
6
Linear Programming
7
Integer Programming
8
Network Optimization
Part III: Uncertainty and Data-Driven Approaches
9
Stochastic Programming
10
Robust Optimization
11
Simulation and Monte Carlo Methods
Part IV: Machine Learning Meets Optimization
12
Predict-then-Optimize
13
Reinforcement Learning and Dynamic Programming
14
Learning-Augmented Optimization
Part V: Data Science Tooling for OR
15
The Data Science Pipeline for Optimization
16
Interactive Visualization for Operations Research
17
Agent-Augmented OR Workflows
Part VI: Applications
18
Supply Chain Optimization
19
Resource Scheduling
Part VII: Capstone
20
Capstone: End-to-End ML + OR Pipeline
21
References
Table of contents
21.1
Bibliography
21
References
Code
21.1
Bibliography
20
Capstone: End-to-End ML + OR Pipeline
Source Code
---
title:
"References"
---
## Bibliography
::: {#refs}
:::