This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the current version (Jan-Apr 2023). Earlier versions can be found at these links:
- from Sep-Dec 2020 taught by Mike Gelbart
- from Jan-April 2022 taught by Giulia Toti
- from May-June 2022 taught by Mehrdad Oveisi
- from Sep-Dec 2022 taught by Varada Kolhatkar
Instructors: Giulia Toti (201), Mathias Lecuyer (202), Amir Abdi (203)
- Course GitHub page
- Course Jupyter book. Important: this is a static version of the lecture notebooks developed by a previous instructor of the course. It can be used as reference for the content, but not for anything related to the particular course instance (due dates, setup steps, etc.)
- Course videos YouTube channel
- Canvas link
- Syllabus / administrative info
- Piazza (this is where all announcements will be made). Click here to enroll.
- Other course documents
- Past exams
Usually the homework assignments will be due on Mondays and will be released on Tuesdays.
Assessment | Due date | Where to find? | Where to submit? | Weight (%) |
---|---|---|---|---|
Syllabus quiz | Jan 16, 11:59pm | Canvas | Canvas | 1% |
hw1 | Jan 16, 11:59pm | Github repo | Gradescope | 3% |
hw2 | Jan 23, 11:59pm | Github repo | Gradescope | 3% |
hw3 | Feb 1, 11:59pm | Github repo | Gradescope | 4% |
hw4 | Feb 10, 11:59pm | Github repo | Gradescope | 4% |
Midterm | Feb 15 Wednesday | TBD | TBD | 19 % |
hw5 | March 1, 11:59pm | Github repo | Gradescope | 4% |
hw6 | Mar 15, 11:59pm | Github repo | Gradescope | 5% |
hw7 | Mar 22, 11:59pm | Github repo | Gradescope | 4% |
hw8 | TBD, 11:59pm | Github repo | Gradescope | 3% |
Final exam | Apr 20, 7:00pm | TBD | TBD | 50% |
Lectures will be on Tuesday and Thursday. Exact time and location change according to your section:
Section | Day | Time | Location |
---|---|---|---|
201 | Tue/Thu | 2:00 - 3:30 | Geography 100 |
202 | Tue/Thu | 3:30 - 5:00 | P. A. Woodward Instructional Resources Centre 3 |
203 | Tue/Thu | 5:00 - 6:30 | Hugh Dempster Pavilion 310 |
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.
Date | Topic | Assigned videos and datasets | vs. CPSC 340 |
---|---|---|---|
Jan 10 | Course intro | 📹 |
n/a |
Part I: ML fundamentals and preprocessing | |||
Week 1 datasets: |
|||
Jan 12 | Decision trees | 📹 |
less depth |
Jan 17 | ML fundamentals | 📹 |
similar |
Week 2 datasets: |
|||
Jan 19 |
|
📹 |
less depth |
Jan 24 | Preprocessing, sklearn pipelines |
📹 |
more depth |
Week 3 dataset: |
|||
Jan 26 | More preprocessing, sklearn ColumnTransformer , text features |
📹 |
more depth |
Week 4 datasets: |
|||
Jan 31 | Linear models | 📹 |
less depth |
Week 5 datasets: |
|||
Feb 2 | Hyperparameter optimization, overfitting the validation set | 📹 |
different |
Feb 7 | Evaluation metrics for classification | 📹 |
more depth |
Week 6 datasets: |
|||
Feb 9 | Regression metrics | 📹 |
more depth on metrics less depth on regression |
Feb 14 | Midterm review | ||
Feb 15 | Midterm | On Wednesday! Note the different time! More details will be posted on Piazza | |
Feb 16 | No lecture | ||
Feb 19-25 | Reading week (no classes) | ||
Week 7 datasets: |
|||
Feb 28 | Ensembles | 📹 |
similar |
Mar 2 | Feature importances, model interpretation | 📹 |
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 9 | Clustering | 📹 |
less depth |
Mar 14 | More clustering | None | less depth |
Week 9 datasets: |
|||
Mar 16 | Simple recommender systems | less depth | |
Mar 21 | Text data, embeddings, topic modeling | 📹 |
new |
Mar 23 | Neural networks and computer vision | less depth | |
Mar 28 | Time series data | (Optional) Humour: The Problem with Time & Timezones | new |
Mar 30 | Survival analysis | 📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring | new |
Part III: Communication, ethics, deployment | |||
April 4 | Ethics | 📹 (Optional but highly recommended) |
new |
Apr 6 | Communication | 📹 (Optional but highly recommended) |
new |
Apr 11 | Model deployment | new | |
Apr 13 | Conclusions - TBD | new |