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 identifying objects in images? Or simply solving a Sudoku?

Come to this session to see live examples of optimization, stochastic, and machine learning models on the JVM to solve real-world problems like discrete optimization, Naive Bayes, Monte Carlo simulations, and artificial neural networks.