CS489/698 - Assignments

There will be five assignments given the course, each worth 8% of the final mark (6% for CS698). Assignments are done individually (i.e., no team). Each assignment will have a theoretical part and a programming part.

The approximate out and due dates are:

A1: out Jan 9, due Jan 20 (11:59 pm)
A2: out Jan 23, due Feb 3 (11:59 pm)
A3: out Feb 6, due March 3 (11:59 pm)
A4: out March 6, due March 17 (11:59 pm)
A5: out March 20, due March 31 (11:59 pm)

On the due date of an assignment, the work done to date should be submitted electronically on the LEARN website; further material may be submitted with a 2% penalty for every rounded up hour past the deadline. For example, an assignment submitted 5 hours and 15 min late will receive a penalty of ceiling(5.25) * 2% = 12%. Assignments submitted more than 50 hours late will not be marked.

Assignment 1: due Jan 20 (11:59 pm)

Assignment handout
Dataset for K-nearest neighbour: knn-dataset.zip

Problem: this data is a modified version of the Optical Recognition of Handwritten Digits Dataset from the UCI repository. It contains pre-processed black and white images of the digits 5 and 6. Each attribute indicates how many pixels are black in a patch of 4 x 4 pixels.
Format: there is one row per image and one column per attribute. The class labels are 5 and 6.
Parsing: if you use Matlab, you can load the datasets easily with the command "load dataX.csv".

Dataset for linear regression: regression-dataset.zip

Problem: this data corresponds to samples from a 2D surface that you can plot to visualize how linear regression is working.
Format: there is one row per data instance and one column per attribute. The labels are real values.
Parsing: if you use Matlab, you can load the datasets easily with the command "load filename.csv".
Visualization: 3D scatter plot.

Paulo Pacheco (ppacheco [at] uwaterloo [dot] ca) is the TA in charge of Assignment 1. He will hold special office hours on Wednesday Jan 18, 5-6:30pm in the AI lab (DC2306C).
Paulo Pacheco will hold special office hours on Wednesday Feb 1, 5-6:30pm in the AI lab (DC2306C) to answer questions about the marking of assignment 1.

Assignment 2: due Feb 3 (11:59 pm)

Assignment handout
Dataset: use the dataset for K-nearest neighbours from Assignment 1.
Timmy Tse (trttse [at] uwaterloo [dot] ca) is the TA in charge of Assignment 2. He will hold special office hours on Wednesday Feb 1, 5-6:30pm in the AI lab (DC2306C).
Timmy Tse will hold special office hours on Monday Feb 13, 2:30-4pm in the AI lab (DC2306C) to answer questions about the marking of assignment 2.

Assignment 3: due March 3 (11:59pm)

Assignment handout
Dataset for non-linear regression: regression-dataset.zip

Problem: this data corresponds to samples from a 2D surface that you can plot to visualize how linear regression is working.
Format: there is one row per data instance and one column per attribute. The labels are real values.
Parsing: if you use Matlab, you can load the datasets easily with the command "load filename.csv".
Visualization: here is a 3D view of the data.

Timmy Tse (trttse [at] uwaterloo [dot] ca) is the TA in charge of Assignment 3. He will hold special office hours on Wednesday March 1, 5-6:30pm in the AI lab (DC2306C).
Timmy Tse will hold special office hours on Wednesday March 15, 5-6:30pm in the AI lab (DC2306C) to answer questions about the marking of assignment 3.

Assignment 4: due March 17 (11:59pm)

Assignment handout
Dataset: data.zip

Problem: this dataset consists of sequences of hidden states and noisy observations.
Format: each data file consists of a sequence of noisy observations with one data instance per row. Each label file consists of a sequence of hidden states (labeled 1, 2 or 3).
Parsing: if you use Matlab, you can load the dataset easily with the command "load filename.csv".

Paulo Pacheco (ppacheco [at] uwaterloo [dot] ca) is the TA in charge of Assignment 4. He will hold special office hours on Wednesday March 15, 5-6:30pm in the AI lab (DC2306C).
Paulo Pacheco will hold special office hours on Wednesday March 29, 5-6:30pm in the AI lab (DC2306C) to answer questions about the marking of assignment 4.

Assignment 5: due March 31 (11:59pm)

Assignment handout
Paulo Pacheco (ppacheco [at] uwaterloo [dot] ca) is the TA in charge of Assignment 5. He will hold special office hours on Wednesday March 29, 5-6:30pm in the AI lab (DC2306C).
Paulo Pacheco will hold special office hours on Wednesday April 12, 5-6:30pm in the AI lab (DC2306C) to answer questions about the marking of assignment 5.