CS485/685 - Assignments
There will be five assignments given the course, each worth 8% of
the final mark (6% for CS685). 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 12, due Jan 25 (11:59 pm)
- A2: out Jan 26, due Feb 8 (11:59 pm)
- A3: out Feb 23, due March 7 (11:59 pm)
- A4: out March 8, due
March 21 (11:59 pm)
March 23 (11:59 pm)
- A5:
out March 22, due April 4 (11:59 pm) out
March 24, due April 6 (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 for half credit within
24 hours. Assignments submitted more than 24 hours late will
not be marked.
Assignment 1: due Jan 25
(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 preprocessed 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
regression-dataset2.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.
- Priyank Jaini (pjaini [at] uwaterloo [dot] ca) is the TA in
charge of Assignment 1. He will
hold special office hours on Friday Jan 22, 11am - 1pm in the
AI lab (DC2306C).
Assignment 2: due Feb 8
(11:59 pm)
- Assignment handout
- Dataset: use the dataset for K-nearest neighbours in
Assignment 1.
- William Herring
(wherring [at] uwaterloo [dot] ca) is the TA in charge
of Assignment 2. He will
hold special office hours on Friday February 5, 11am - 1pm in
the AI lab (DC2306C).
Assignment 3: due March 7 (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.
- Priyank Jaini (pjaini [at] uwaterloo [dot] ca) is the TA in
charge of Assignment 3. He will hold
special office hours on Friday March 4, 11am - 1pm in the AI
lab (DC2306C).
Assignment 4: due March 21 (11:59pm) March
23(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".
- William Herring
(wherring [at] uwaterloo [dot] ca) is the TA in charge
of Assignment 4. He will hold
special office hours on Friday March 18, 2:30 - 4pm in the AI
lab (DC2306C).
Assignment 5: due April 6 (11:59pm)
- Assignment handout
- Priyank Jaini (pjaini [at] uwaterloo [dot] ca) is the TA in
charge of Assignment 5. He will hold
special office hours on Friday April 1, 11am - 1pm in the AI
lab (DC2306C).