The goal of assignments is to assess students’ understanding of the course outcomes in homework and final project assignments. The assignment deadlines are due on Gradescope and as follows:
Reading Assignments
Reading Assignment 1: Chapter 2 End-to-End of “Machine Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.” O’Reilly Media, Inc., 2022. (Available on Canvas->Library Reserves)
- Reading Quiz 1 Assigned: 1/26/2023 @ 5PM on Canvas
- Reading Quiz 1 DUE: 2/2/2023 @ 5PM
Reading Assignment 2: Chapter 4 Training Models of “Machine Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.” O’Reilly Media, Inc., 2022. (Available on Canvas->Library Reserves)
- Reading Quiz 2 Assigned: 2/13/2023 @ 5PM on Canvas
- Reading Quiz 2 DUE: 2/20/2023 @ 5PM
Reading Assignment 3: Chapter 3 Classification of “Machine Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.” O’Reilly Media, Inc., 2022. (Available on Canvas->Library Reserves)
- Reading Quiz 3 Assigned: 2/20/2023 @ 5PM on Canvas
- Reading Quiz 3 DUE: 3/6/2023 @ 5PM
Reading Assignment 4: Chapter 9 Unsupervised Learning Techniques of “Machine Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.” O’Reilly Media, Inc., 2022. (Available on Canvas->Library Reserves)
- Reading Quiz 4 Assigned: 3/13/2023 @ 5PM on Canvas
- Reading Quiz 4 DUE: 3/120/2023 @ 5PM
Reading Assignment 5: Chapter 14 Deep Computer Vision Using Convolutional Neural Networks of “Machine Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.” O’Reilly Media, Inc., 2022. (Available on Canvas->Library Reserves)
- Reading Quiz 5 Assigned: 3/27/2023 @ 5PM on Canvas
- Reading Quiz 5 DUE: 4/10/2023 @ 5PM
Programming Assignments
Homework 1: Building End-to-End ML Pipeline & Dataset Curation. The goal of Homework 1 assignment is to build your first end-to-end Machine Learning (ML) pipeline using public datasets and by creating your own datasets. The learning outcomes for this assignment are [Github]:
- Build framework for end-to-end ML pipeline in Streamlit. Create your first web application!
- Develop web application that walks users through steps of ML pipeline starting with data visualization and preprocessing steps.
Assigned: 2/3/2023 @ 5PM on Canvas and GitHub; Submission link: https://classroom.github.com/a/fiL30jIe
DUE: 2/17/2023 @ 11PM on Github; push changes to your local copy of the repository.
Deadlines
- Reading Quizzes: Covers course topics in reading assignments and will be made available before after discussed in class.
- Homeworks: Coding assignments on course topics, assigned on Wednesday and the following Wednesday @ 12PM
- Final Project Proposal: Propose an FP on a PAML; DUE on April 28th @ 5PM.
- Final Project Midpoint Report: Provide updates on project deliverables on May 3th during the class session.
- Final Project Presentation: Present FP in class for 15 minutes with a 3-minute Q&A; scheduled on May 15th @ 12PM.
- Final Project Report: Submit FP report; DUE on May 17th @ 12PM
Late Policy
Students have 6 late days (2 max per assignment) to use for the semester for assignment submissions, including homework and the final project. After that, the grade will be dropped one letter grade per day late.
Students have 1 week after assignments are returned to make a regrade request (no exceptions). Send an email to Prof. Taylor, Jinzhao Kang, and Kathryn Guda.
Course Assignment Schedule (Tentative)
Week # |
Lecture (M) |
Lecture (W) |
1: Week of 1/23 |
Introduction to PAML
|
Revisit Preliminaries; In-class activity [Github]Reading Quiz 1 Assigned on Thursday; DUE on 2/2 @ 12PM |
2: Week of 1/30 |
Building an End-to-End ML Pipeline I |
Building an End-to-End ML Pipeline II
|
3: Week of 2/6 |
Dataset Creation, Acquisition, and Handling
|
Dataset Creation, Acquisition, and Handling |
4: Week of 2/13 |
Introduction to Regression I
|
Introduction to Regression II |
5: Week of 2/20 |
Regression for Predicting Housing Prices I |
Regression for Predicting Housing Prices II |
6: Week of 2/27 |
No Class – February Break |
Scaling to Large Datasets |
7: Week of 3/6 |
Introduction to Classification I
|
Introduction to Classification II |
8: Week of 3/13 |
Classification for Product Recommendation I
|
Classification for Product Recommendation II |
9: Week of 3/20 |
Introduction to Clustering I
|
Introduction to Clustering II |
10: Week of 3/27 |
Clustering for Document Retrieval I
|
Clustering for Document Retrieval II |
11: Week of 4/3 |
No Class – Spring Break |
No Class – Spring Break |
12: Week of 4/10 |
Deep Learning Fundamentals I
|
Deep Learning for Image Search II |
13: Week of 4/17 |
Deep Learning for Image Search II |
Final Project Discussion |
14: Week of 4/24 |
FP Working Day |
Guest Lecture – Tariq Iqbal (UVA) |
15: Week of 5/1 |
FP Working Day |
FP Working Day/Final Project Midpoint Report |
16: Week of 5/8 |
Last Day of Instruction |
No Class |
17: Week of 5/15 |
Final Project Presentation |
Final Project Report & Code Due |