OSB > Neil Yorke-Smith > Teaching
DCSN200: Operations Management provides a foundation for understanding the operations of a firm. One of the main objectives of this course is to recognize the immense competitive advantages companies can draw from an efficient management of operations. The focus will be mainly on the systematic planning, design, and operations of some of the main processes required for the production of goods and the delivery of services. Operations management touches upon many vital business functions of an organization including product and service design, customer order management, processes design and improvement, capacity and material planning, quality control, inventory and supply chain management.
DCSN205: Managerial Decision Making is a spreadsheet-based introduction to the tools and techniques of modern managerial decision making. It addresses formulation of models that can be used to analyze complex problems taken from various functional areas of management, including finance, marketing, operations, and human resources. The goal is to understand how business decisions are reached, what tradeoffs are made, and how outcomes depend on the underlying data. A broad range of analytical methods is covered, including linear programming, integer linear programming, decision analysis, decision trees, queues and Monte Carlo simulation. Software packages like Excel, Tree Plan and Crystal Ball will be used. DCSN200 is recommended as a prerequisite.
From 2006-09, I taught the class CS227: Reasoning Methods in Artificial Intelligence in the Department of Computer Science at Stanford University.
CS227 is a second class in AI, covering methods for (largely) non-probabilistic reasoning in propositional satisfiability, constraint satisfaction, temporal reasoning, and planning. It is complementary to CS228: Probabilistic Models in Artificial Intelligence. Assessment is by group programming projects. The course is appropriate for graduate students (both Masters and PhD students), and for advanced undergraduates with a special interest in AI. Students are assumed to have taken basic courses in AI, algorithms, data structures, and programming, or to have equivalent background in these areas.