Recording brought to you by American Express https://americanexpress.io/kotlin-jobs
As programmers, we love solving problems. However, sometimes we need more than programmer grit to solve many problems with no easy answer. Suppose you need to tightly schedule 190 classes in 20 classrooms, with different class durations, recurrences, and constraints throughout the week? What about minimizing the operating cost of a train schedule while maintaining a steady movement of passengers? How about anticipating an industry competitor's move? Or simply solving a Sudoku?
Mathematical modeling is the workhorse of data science, machine learning, and operations research. By effectively expressing mathematical concepts in code, you can gracefully find solutions to a broad category of problems and avoid impractical brute-force techniques. Even better, Kotlin's pragmatic language features allow clearer and more refactorable models that can safely evolve and be put in production.
Come to this session to see live examples of Kotlin mathematical models to solve real-world problems like discrete optimization, Bayesian techniques, and artificial neural networks.
About the Presenter:
Thomas Nield is a Business Consultant at Southwest Airlines, often balancing technology with operations research. He is also an author and trainer with O'Reilly Media. He wrote two books ("Getting Started with SQL" and "Learning RxJava") and regularly contributes to OSS projects.