Uncertainty penetrates in every corner of data science and decision science, from data generation, model selection, system dynamics, algorithm design, all the way to prediction and decision making. This course will offer a broad overview of the modeling, theories, algorithms, and applications for the vibrant field of optimization and learning under uncertainty. Topics include stochastic optimization, robust linear/conic programs, two-stage stochastic programming , chance constraint programming, risk-averse optimization, data-driven distributionally robust optimization, multi-stage stochastic programming, and Markov decision problems. We will cover a wide range of solution methods including stochastic approximation, Monte Carlo sampling methods, variance reduction techniques, decomposition methods, convex relaxation, dynamic programming, reinforcement learning algorithms, and etc. We will also discuss their wide applications in machine learning, financial engineering, operations management, power systems, and control. Check course website here.

Nearly every problem in machine learning and high-dimensional statistics can be formulated in terms of optimization of some function, possibly under some constraints. In the wake of Big Data era, there is an increasingly strong need to design efficient and scalable algorithms to address optimization problems in large scale - including problems exhibiting inherently high dimensions and problems involving truly massive data sets. The key aim of this course is to expose students to a wide range of algorithmic and theoretical developments in modern convex optimization and bring them near the frontier of research in large-scale optimization and machine learning.

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This course is focused on learning to recognize, understand, analyze, and solve unconstrained and constrained convex optimization problems arising in engineering fields. The course shall keep strong emphasis on in-depth understanding of classical convex analysis, theory and applications of disciplined convex programming, as well algorithms for solving constrained convex problems.

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Data analytics is the science of analyzing data to convert information to useful knowledge. This course is intended to be an introduction to basic probability theory, statistical analysis, and machine learning modeling of data. A student should complete this course with the ability to understand how probability distributions model experiments with uncertain outcomes, how to analyze these experiments by using statistical methods to observed outcomes, and how to apply machine learning tools for prediction.

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• Industry Partner: Spraying Systems Company
  Project: Predictive Data Analytics for VX-70 Nozzle Status.
• Industry Partner: John-Deere Technology Innovation Center
  Project: Tango Cutting Path Map Development.
• Industry Partner: Nokia-US
  Project: Value Stream Mapping for Global Process Improvement.
• Industry Partner: Caterpillar
  Project: Assembly Process Design and Simulation for Caterpillar Engines.
  (Bernt O. Larson Award, 1st Place)