CS486/686 - Assignments
There will be four assignments given the course, each worth 10% of
the
final mark (7% for CS686). Each assignment will have a theoretical part
and
a programming part. Assignments are done individually (i.e., no
team). You are free to program in the language of
your choice, however Matlab is recommended since it provides a
convenient high-level programming environment for matrix
operations. If you decide to program in
Matlab, the IST group maintains a nice set of online references for Matlab including a
tutorial.
The approximate out and due
dates
are:
- A1: out Sept 17, due Oct 6
- A2: out Oct 6, due Oct 27
- A3: out Oct 27, due Nov 17
- A4: out Nov 17, due Dec 3
For each assignment, a hard copy must be
handed in on the due date either in class or in slots 6 or 7 of the
assignment drop off
box #1 (3rd floor of the math building near the bridge to DC).
No
late
assignment will be accepted.
NB: Unclaimed assignments can be picked up at Jessica Miranda's
office (DC3516) between 8:30-12 and 1-4:30pm.
Assignment 1: due Oct 6
- Click here for the assignment
- Click here for the test problems
- The TA responsible for this assignment,
Arthur Carvalho, will hold special office hours
on Friday Oct 2, 1-3pm in the AI lab (DC2306C) to answer
questions. Also make sure to post questions to the newsgroup
(uw.cs.cs486) instead of emailing Arthur or Pascal directly.
Assignment
2: due Oct 27
- Click here for the assignment
- The TA responsible for this assignment,
Omar Zia Khan, will hold special office hours
on Friday Oct 23, 4-6pm in the AI lab (DC2306C) to answer
questions.
Assignment 3: due Nov 17
- Click here
for the assignment
- Train and test your algorithms with a subset of the 20 newsgroup
dataset. More precisely, you will use the documents posted on
the alt.atheism
and comp.graphics
newsgroup. To save you the trouble of writing a parser for
arbitrary text, I converted the relevant documents to a simple encoding
(files below). Each line of the files trainData.txt
and testData.txt
are formatted "docId
wordId" which indicates that word wordId is
present in document docId.
The
files trainLabel.txt
and testLabel.txt
indicate the label/category (1=alt.atheism
or 2=comp.graphics)
for
each document (docId
=
line#). The file words.txt
indicates which word corresponds to each wordId
(denoted by the line#). If you are using Matlab, the file loadScript.m
provides a simple script to load the files into appropriate
matrices. At the Matlab prompt, just type "loadScript" to execute
the script. Feel free to use any other language and to build your
own parser if you prefer.
- The TA responsible for this assignment,
Tyrel Russell, will hold special office hours
on Friday Nov 13, 1-3pm in the AI lab (DC2306C) to answer
questions.
Assignment 4: due Dec 3
- Click here
for the assignment
- The TA responsible for this assignment,
Tyrel Russell, will hold special office hours
on Monday Dec 14, 1-3pm in the AI lab (DC2306C) to answer
questions.