ECE365: Fundamentals of Machine Learning (Labs and Quizzes)
| Topic | Lab | Due |
| Week 1 | Introduction to Python | [link] | Not graded (Solutions) |
| Week 2 | Classification, Part 1 | [link] | Sep 9th 11:59 pm CT |
| Week 3 | Classification, Part 2 | [link] | Sep 16th 11:59 pm CT |
| Week 4 | Linear Regression and Clustering | [link] | Sep 23th 11:59 pm CT |
| Week 5 | Principal Component Analysis | [link] | Sep 30th 11:59 pm CT
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Quizzes
Quizzes will be conducted online on gradescope through CBTF. Please add yourself to the class on the CBTF scheduler. See sample quizzes with solutions (from SP 2020) below:
The quiz times for part 1 are:
Lab Submission
Update on Submission:
Upload notebooks (do not include the dataset!) with file name as your netid on Compass2g.
Meanwhile, download your completed (including those short answer questions) notebook as a pdf and upload it to gradescope.
Your grades and feedbacks will mainly appear in gradescope. However, your notebook on Compass is necessary for us to re-run your codes. Apologies for any inconvenience with uploading to two different places.
Finally, if you don't know how to download your notebook as a pdf, please refer to here.
Lab 1
Hints:
Exercises 5 and 6 will be building blocks for the first problem in Lab 2 (where you can use part (a) or part (b) of both exercises). You should be able to do part (a) of both exercises in a straightforward manner. As stated in the lab, part (b) is optional, but good to know. If you're stuck on part (b), make sure to write out the matrices and you should be able to construct the appropriate matrix multiplication. If you do not solve part (b), do not worry about it. But, you really should solve part (a) of both Exercises 5 and 6.
A better hint for Exercise 6(b) might be: “You can do this with the np.dot, elementwise multiplication and np.sum (along an axis) operations.”
Please follow the Python instructions to get started with Jupyter notebooks. You should not need to install any additional packages for this portion of the course if you have installed Anaconda or Canopy.
The following other Python tutorials may be helpful:
And a few links to write code concisely:
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