There will be five assignments, 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 8, due Jan 19 (11:59 pm)
- A2: out Jan 22, due Feb 2 (11:59 pm)
- A3: out Feb 12, due March 2 (11:59 pm)
- A4: out March 5, due March 16 (11:59 pm)
- A5: out March 19, due March 30 (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 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.

- Hamidreza Shahidi (h24shahi [at] uwaterloo [dot] ca) is the TA responsible for A1. He will hold
**special office hours to answer questions about A1 on Thursday Jan 18, 12-2pm in the AI lab (DC2306C)**. Pascal also holds regular office hours every Tuesday 1-2:30pm in DC2514. - Hamidreza Shahidi will hold special office hours on Thursday Feb 1, 12-2pm in the AI lab (DC2306C) to answer questions about the marking of assignment 1.

- Assignment handout
- Dataset: use the dataset for K-nearest neighbours from Assignment 1.
- Nicole McNabb (nmcnabb [at] uwaterloo [dot] ca) is the TA responsible for A2. She will hold
**special office hours to answer questions about A2 on Thursday Feb 1, 12-2pm in the AI lab (DC2306C)**. Pascal also holds regular office hours every Tuesday 1-2:30pm in DC2514. - Nicole MacNabb will hold special office hours on Monday Feb 12, 12-2pm in the AI lab (DC2306C) to answer questions about the marking of assignment 2.

- Assignment handout
- Dataset for linear regression: regression-dataset.zip
- Problem: this data corresponds to samples from a 2D surface that you can plot to visualize how 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 view of the data.

- Amir-Hossein Karimi (a6karimi [at] uwaterloo [dot] ca) is the TA responsible for A3. He will hold
**special office hours to answer questions about A3 on Thursday March 1, 12-2pm in the AI lab (DC2306C)**. - Amir-Hossein Karimi will hold special office hours on Thursday March 15, 12-2pm in the AI lab (DC2306C) to answer questions about the marking of assignment 3.

- Assignment handout
- Hamidreza Shahidi (h24shahi [at] uwaterloo [dot] ca) is the TA responsible for A4. He will hold
**special office hours to answer questions about A4 on Thursday March 15, 12-2pm in the AI lab (DC2306C)**. - Hamidreza Shahidi will hold special office hours on Thursday March 29, 12-2pm in the AI lab (DC2306C) to answer questions about the marking of assignment 4.

- Assignment handout
- Hamidreza Shahidi (h24shahi [at] uwaterloo [dot] ca) is the TA responsible for A5. He will hold
**special office hours to answer questions about A5 on Thursday March 29, 12-2pm in the AI lab (DC2306C)**.