Performance study - individual instruction.

Performance study - individual instruction.

Studies diatonic harmony (part-writing, inversions, harmonization, figured bass and non-harmonic tones), continues with seventh chords, and begins the exploration of chromaticism. Includes analysis, ear-training, solfege, and keyboard harmony.

This course will explore a range of approaches and techniques involved in the creation of electronic and computer music, including aspects of form and development, analog and digital synthesis and signal processing, basic computer music programming, and audio recording and production techniques. The focus of this seminar will be a series of exercises and creative projects that develop aesthetic and technical abilities. This creative work will be supported and enriched by selected reading and listening examples, as well as ongoing technical labs and demonstrations.

With varied repertoire, an intermediate-level women's choir providing excellent vocal training, occasional solo opportunities, and a structured sight-singing curriculum. Performs on and off campus, sometimes with men's choruses and orchestra.

Studies and presents a variety of orchestra repertoire on and off campus. Multiple opportunities to perform each semester.

This course will focus on learning by ear and playing the polyrhythmic traditional music of the peoples of southern Ghana, Togo and Benin, including sections of Adjogbo and Agbekor. All students will learn drum, rattle and bell parts, some songs and some dance steps as well. Non musicians are welcome, but practicing between classes is required. The group will perform in a workshop at the end of the semester.

Principles and practices for the design and implementation of compilers and interpreters. Will cover the stages of the compilation and execution process: lexical analysis; parsing; symbol tables; type systems; scope; semantic analysis; intermediate representations; run-time environments and interpreters; code generation; program analysis and optimization; and garbage collection. Students will construct a full compiler.

How does Neflix learn what movies a person likes? How do computers read handwritten addresses on packages, or detect faces in images? Machine learning is the practice of programming computers to learn and improve through experience, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodology, and Bayesian probabilistic modeling.