ECE 365: Fundamentals of Machine Learning (Labs and Quizzes)
| Topic | Lab | Due (All US Central Time) | |
| Week 1 | Introduction to Python | [link] | Not graded (Solution here) |
| Week 2 | Classification, Part 1 | [link] | Sep 7th 11:59 pm CT |
| Week 3 | Classification, Part 2 | [link] | Sep 14th 11:59 pm CT |
| Week 4 | Linear Regression and Clustering | [link] | Sep 21st 11:59 pm CT |
| Week 5 | Principal Component Analysis | [link] | Sep 28th 11:59 pm CT |
Quizzes
You will have four quizzes. The quizzes are closed-book and closed-notes. Electronic devices (calculators, cellphones, pagers, laptops, headphones, etc.) are neither necessary nor permitted.
The quizzes from past (with their solutions) are:
Lab Submission
Please submit on Gradescope and change your gradescope Student ID to your University UIN.
You will be provided with a template Python script (main.py) for each lab where you need to implement the provided functions as needed for each question. Follow the instructions provided in the Jupyter Notebook (.ipynb) to implement the required functions. Do not change the file name or the function headers!
Upload only your Python script (.py file) on Gradescope. Don't upload your datasets or Jupyter Notebook (.ipynb file). We have provided an example solution for Lab 1 above. You will similarly submit your .py file for each future lab.
Your grades and feedbacks will appear on Gradescope. The grading for the programming questions is automated using Gradescope autograder, no partial credits are given. Therefore, if you wish, you will have a chance to re-submit your code within 72 hours of receiving your first grade for the corresponding lab, only if you have reasonable submissions before the deadline (i.e. not an empty script).
You can submit for any times you want before the deadline. After the deadline, you will see a re-submission assignment open on Gradescope, and you can still submit multiple times to that re-submission assignment within 72 hours. If you re-submit after the deadline, the final grade for the programming part of this lab will be calculated as max{first_grade, .4 * first_grade + .6 * .9 * re-submission_grade}.
Each lab will also have Multiple Choice Questions (MCQs) that are needed to be completed on Gradescope within the deadline. This score will be added to the programming part to produce the overall score for this assignment.
Although each assignment has different overall scores, they will be normalized (by the full overall score) to calculate the final score for part 1 of this course.
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, element-wise 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:
Elegant Scipy: The Art of Scientific Python, by Juan Nunez-Iglesias, Stefan van der Walt, Harriet Dashnow (Highly Recommended)
Software Carpentry (Highly Recommended)
NumPy Quickstart (Highly Recommended)
High Performance Scientific Computing AMath 483/583, Spring 2011 at U. Washington
And a few links to write code concisely:
Code Vectorization (in Matlab; the ideas translate to Python in a straighforward manner)