Global Arc

Topics chosen from, but not limited to: a group of works by a single composer (Leonin's organa, Monteverdi madrigals, Brahms's symphonies); a certain genre (19th-century choral works, Hindustani Khayal, contemporary rock, late 16th-century madrigal); a specific theoretical or historical problem (atonal theory, composers' sketches and musical analysis, the origins of opera).

This seminar explores a host of questions surrounding music's capacity to convey and shape narratives. Students will engage critically with literature from psychology, musicology, music theory, and media studies to make sense of narrative perceptions of music--when they arise, why, and what it means for broader theories of communication. The class will consider narrative perceptions in a host of different contexts, including instrumental music, song, film music, and video game music.

A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis testing and regression and classification. Applicability and limitations of these methods will be illustrated using a variety of modern real world data sets and manipulation of the statistical software R. Prerequisite: MAT 201 concurrent or equivalent. Two lectures, one precept.

This course focuses on analytical and computational tools for optimization. We will introduce least-squares optimization with multiple objectives and constraints. We will also discuss linear optimization modeling, duality, the simplex method, degeneracy, interior point methods and network flow optimization. Finally, we will cover integer programming and branch-and-bound algorithms. A broad spectrum of real-world applications in engineering, finance and statistics is presented. Prerequisite MAT 202 or 204. Two lectures, one precept.

An introduction to probability and its applications. Topics include: basic principles of probability; Lifetimes and reliability, Poisson processes; random walks; Brownian motion; branching processes; Markov chains. Prerequisite: MAT 201, MAT 203, or MAT 216. Two lectures, one precept.

A survey of quantitative approaches for making optimal decisions under uncertainty, including decision trees, Monte Carlo simulation, and stochastic programs. Forecasting and planning systems are integrated in the context of financial applications. Machine learning methods are linked to the stochastic optimization models. Prerequisites: ORF 307 or MAT 305, and ORF 309. Two 90-minute classes, one precept.

Financial Mathematics is concerned with designing and analyzing products that improve the efficiency of markets, and create mechanisms for reducing risk. This course develops quantitative methods for these goals: the notions of arbitrage and risk-neutral pricing in discrete time, specific models such as Black-Scholes and Heston in continuous time, and calibration to market data. Credit derivatives, the term structure of interest rates, and robust techniques in the context of volatility options will be discussed, as well as lessons from the financial crisis. Prerequisite: ORF 309. Two lectures, one precept.

This course is a theoretically oriented introduction to the statistical tools that underpin modern machine learning, whose hallmarks are large datasets and/or complex models. Topics include a rigorous analysis of dimensionality reduction, a survey of models ranging from regression to neural networks, and an analysis of learning algorithms.. Prerequisites: Probability at the level of ORF 309. Statistics at the level of ORF 245. Linear Algebra at the level of MAT 202 or permission of instructor. Two lectures, one precept.

An introduction to several fundamental and practically-relevant areas of modern optimization and numerical computing. Topics include computational linear algebra, first and second order descent methods, convex sets and functions, basics of linear and semidefinite programming, optimization for statistical regression and classification, and techniques for dealing with uncertainty and intractability in optimization problems. Extensive hands-on experience with high-level optimization software. Applications drawn from operations research, statistics and machine learning, economics, control theory, and engineering.

A course covering special topics in operations research or financial engineering. Subjects may vary from year to year.