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Three sections of CS 489 in Winter 2016 term

In W16, the Cheriton School of Computer Science will be offering three sections of CS489, Topics in Computer Science:

- Big Data Infrastructure, with Dr. Jimmy Lin
- Complexity of Computational Problems, with Dr. Eric Blais
- Computational Audio, with Dr. Richard Mann

A short description of each course follows. Note that each course description also contains a link to a fuller description.

CS489 - Big Data Infrastructure, Dr. Jimmy Lin This course provides an introduction to infrastructure that makes data-intensive computing (aka "big data") possible, covering abstractions, frameworks, and algorithms. We'll discuss processing of many types of data (textual, relational, graph, etc.) as well as different styles of computation (batch, online, etc.). We'll be working extensively with MapReduce, Spark, noSQL, and other emerging open- source frameworks for distributed processing.

For more details, see

CS489 - Complexity of Computational Problems, Dr. Eric Blais
Prereq: CS 341 is required. CS 360 or CS 365 are recommended but not required.

This course will introduce techniques for establishing fundamental limitations of algorithms for solving computational problems in various settings. Specifically, we will learn and apply methods for determining the minimum time and space requirements of deterministic and randomized algorithms, approximation algorithms, sublinear-time algorithms, streaming algorithms, and parallel algorithms.

This course will be of interest to students who wish to understand the mathematical foundations of computer science and its connections to different topics in combinatorics, probability theory, information theory, and complexity theory.

CS489 - Computational Audio, Dr. Richard Mann
Prerequisite: Scientific Computation (CS370, or equivalent). Matlab programming an asset.
Evaluation: Assignments (60%), Final project (40%) Details (TBA):

This course will provide a self contained introduction to sound processing by computer. It is aimed at senior undergraduates with a strong interest in applied math and scientific computation and looking for a project based course.

The course will begin with a brief introduction to human hearing, acoustics and electronics.
We will then cover analog to digital and digital to analog conversion, followed by time and frequency domain analysis of signals (Fourier transform).

Given this foundation a number of practical problems will be studied, including:
Sound analysis
(time frequency and wavelet representation), sound synthesis (amplitude and frequency
modulation) and System identification (measure frequency response of circuits, microphones, speakers).

Optional topics (lecture and/or project material) include: Digital signal processing (z-transform), audio compression (MP3), digital audio hardware and software systems, and acoustics simulation.


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