Mathematical Modeling with Kotlin
summary
Shows concrete models and working code: a 25 cities demo for the Traveling Salesman uses two‑opt then simulated annealing and regularly finds ~17,509 km solutions; a classroom scheduler models 15‑minute slots as binary variables to place 190 classes in 20 classrooms with recurrence constraints and solves via ojAlgo (code on GitHub); a Naive Bayes bank‑transaction classifier learns categories online; a small neural net for light vs dark font trains on ~1,345 labeled colors and uses ojAlgo or DL4J for production experiments.
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chapters (12)
- 0:00Intro
- 0:43Thomas Nield
- 1:55What is Mathematical Modeling?
- 2:37Why Learn Mathematical Modeling?
- 4:49Traveling Salesman Problem
- 13:27Generating a Schedule
- 19:52Source Code
- 23:55Discrete Optimization Summary
- 29:21Implementing Naive Bayes
- 34:17A Simple Neural Network
- 36:44Activation Functions
- 40:06Learn More About Neural Networks
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