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): http://cs.uwaterloo.ca/~mannr
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).
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