This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the version for 2023W2 (Jan-Apr 2024). Some previous offerings:
- 2020w1 by Mike Gelbart
- 2023s by Mehrdad Oveisi
- 2023w1 by Varada Kolhatkar and Andrew Roth
- 2022w2 by Giulia Toti, Mathias Lecuyer, and Amir Abdi
Mathias Lecuyer (201), Mehrdad Oveisi (202).
Section | Days | Lecture | Location |
---|---|---|---|
201 | Tue, Thu | 09:30 - 10:50 | LSK 200 |
202 | Tue, Thu | 15:30 - 16:50 | MCML 360 |
Mehrdad Oveisi:
- [email protected]
- Please email Mehrdad Oveisi at the above email address for all administrative concerns such as CFA accommodations, extensions or exemptions due to sickness or extenuating circumstances.
- Syllabus
- Course GitHub page
- Course videos YouTube channel
- Canvas
- Piazza: this is where all announcements will be made.
- Setting up the coding environment
- Other course documents
Assessment | Due date | Where to find? | Where to submit? |
---|---|---|---|
Syllabus quiz | Jan 15, 11:59pm | PrairieLearn (PL) | PL |
hw1 | Jan 15, 11:59pm | Github repo | PL |
hw2 | Jan 22, 11:59pm | Github repo | PL |
hw3 | Feb 5, 11:59pm | Github repo | PL |
hw4 | Feb 12, 11:59pm | Github repo | PL |
hw5 | Feb 26, 11:59pm | Github repo | PL |
Midterm | Fri. March 1 | ESB 1013 or HEBB 100 (see piazza) | In person |
hw6 | Mar 25, 11:59pm | Github repo | PL |
hw7 | Apr 2, 11:59pm | Github repo | PL |
hw8 | April 10, 11:59pm | Github repo | PL |
Final exam | TBD | TBD | TBD |
Lectures:
- Watch the "Pre-watch" videos before each lecture.
- You will find lecture notes from each instructor in this repository. Lectures will be posted as they become available.
Lectures:
- The lectures will be in-person (see Class Schedule above for more details).
- All lecture files are subject to change without notice up until they are covered in class.
- You are expected to watch the "Pre-watch" videos before each lecture.
- You are expected to attend the lectures.
- You will find the lecture notes under the lectures in this repository. Lectures will be posted as they become available.
Date | Topic | Videos | vs. CPSC 340 |
---|---|---|---|
Jan 9 | Course intro | ๐น |
n/a |
Part I: ML fundamentals and preprocessing | |||
Jan 11 | Decision trees | ๐น |
less depth |
Jan 16 | ML fundamentals | ๐น |
similar |
Jan 18 | Snow day: no lecture | ||
Jan 23 |
|
๐น |
less depth |
Jan 25 | Preprocessing, sklearn pipelines |
๐น |
more depth |
Jan 30 | More preprocessing, sklearn ColumnTransformer , text features |
๐น |
more depth |
Feb 1 | Linear models | ๐น |
less depth |
Feb 6 | Hyperparameter optimization, overfitting the validation set | ๐น |
different |
Feb 8 | Evaluation metrics for classification | ๐น |
more depth |
Feb 13 | Regression metrics | ๐น |
more depth on metrics less depth on regression |
Feb 15 | Ensembles | ๐น |
similar |
Feb 19-23 | Midterm Break (no classes) | ||
Feb 27 | Midterm review | ||
Feb 29 | No lecture (midterm catch-up) | ||
Mar 1 | Midterm | 16:20 on Friday. ESB 1013 or HEBB 100. More details on Piazza | |
Mar 5 | Feature importances, model interpretation | ๐น |
feature importances is new, feature engineering is new |
Mar 7 | Feature engineering and feature selection | None | less depth |
Part II: Unsupervised learning, transfer learning, different learning settings | |||
Mar 12 | Clustering | ๐น |
less depth |
Mar 14 | More clustering | less depth | |
Mar 19 | Simple recommender systems | None | less depth |
Mar 21 | Text data, embeddings, topic modeling | ๐น |
new |
Mar 26 | Neural networks and computer vision | less depth | |
Mar 28 | Time series data | (Optional) Humour: The Problem with Time & Timezones | new |
Apr 2 | Survival analysis | ๐น (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring | new |
Part III: Communication, ethics, deployment | |||
Apr 4 | Ethics | ๐น (Optional but highly recommended) |
new |
Apr 9 | Communication | ๐น (Optional but highly recommended) |
new |
Apr 11 | Model deployment | new |