Computing Workshop is currently taught in collaboration with McGill’s CDSI, so watch their training page for upcoming summer camps and workshops!
Current Course
Intro to Machine Learning with Python (CDSI) - Fall 2024
This course is taught as a series of five, two-hour workshops, every two weeks, beginning on 3 October 2024. The class times follow the McGill convention, beginning at 10:05 and ending at 11:55.
- Thursday, 3 October 2024 - Lesson 1: Landscape of artificial intelligence and machine learning. Appropriateness of ML. K-nearest neighbours algorithm.
- Thursday, 17 October 2024 - Lesson 2:
Data acquisition and decision trees
- slides
- sample dataset, courtesy of Obviously AI
- data exploration notebook and [solution][f24-ml-1-data-notebook-annotated]
- decision tree notebook
- Thursday, 31 October - Lesson 3: Unsupervised learning and model validation, data leakage
- Thursday, 14 November - Lesson 4: Neural networks part 1
- Thursday, 28 November - Lesson 5: Neural networks part 2, dimensionality reduction, and AI ethics
Past Courses
Intensive Intro to Machine Learning with Python (CDSI) - Summer 2024
This course is taught as a one-week intensive summer workshop, Monday 12 August to Friday 16 August. Mornings (9:30 to noon) are more theoretical, mixing lecture with small group activities, and including a short break. Lunch takes place from noon to 1pm. Afternoons (from 1 to 3:30pm) tend towards more practical, hands-on coding labs. We recap each day around 3:30pm and conclude between 3:30 and 4pm.
- Monday, 12 August - Day 1: Landscape of artificial intelligence and machine learning. Appropriateness of ML. K-nearest neighbours algorithm.
- Tuesday, 13 August - Day 2:
Data acquisition and decision trees
- slides
- sample dataset, courtesy of Obviously AI
- data exploration notebook and solution
- decision tree notebook
- Wednesday, 14 August - Day 3: Unsupervised learning and model validation, data leakage
- Thursday, 15 August - Lesson 3: Neural networks part 1
- Friday, 16 August - Lesson 4: Neural networks part 2 and AI ethics
Fundamentals of Machine Learning with Python (CDSI) - Winter 2024
This course is taught across five two-hour sessions, once weekly.
- 2 February - Lesson 0: Welcome! ML pipeline overview, appropriateness of ML, K-nearest neighbours algorithm
- 9 February - Lesson 1: Data acquisition and decision trees
- 16 February - Lesson 2: Unsupervised learning and model validation
- 23 February - Lesson 3: Neural networks, data leakage, the train/test split
- 1 March - Lesson 4: Neural networks part 2 and AI ethics
Fundamentals of Machine Learning with Python (CDSI) - Summer 2023
This course is taught in an intensive one-week summer school format with 4 hours of instruction per day, split into a morning and an afternoon session. This instalment of Computing Workshop is presented in collaboration with McGill’s CDSI.
Lesson 0: Welcome! ML pipeline overview, appropriateness of ML, Python refresher (slides)
Lesson 1: Data acquisition and k-nearest neighbours (slides) (KNN worksheet) (KNN Jupyter Notebook)
Lesson 2: Data cleaning and decision trees (slides) (sample dataset, courtesy of Obviously AI) (data exploration notebook) (decision tree notebook)
Lesson 3: Unsupervised learning and model validation (slides) (clustering worksheet) (lab resources) (DBSCAN notebook)
Lesson 4: Neural networks and model training, reflecting on AI (slides) (Neural networks notebook)
Fundamentals of Machine Learning with Python (CDSI) - Winter 2023
This course is taught in a weekly format with 2 hours of instruction per session, in collaboration with McGill’s CDSI.
Lesson 0: Welcome! ML pipeline overview, appropriateness of ML, k-nearest neighbours (slides) (KNN worksheet) (KNN Jupyter Notebook)
Lesson 1: Data cleaning, regression, and decision trees (slides) (sample dataset, courtesy of Obviously AI) (data exploration notebook) (decision tree notebook)
Lesson 2: Unsupervised learning and model validation (slides) (clustering worksheet) (lab resources) (DBSCAN notebook)
Lesson 3: Neural networks and model training (slides) (Neural networks notebook)
Intro Python (CDSI) - Summer 2022
This course was taught in an intensive one-week format with 4 hours of instruction per day, in collaboration with McGill’s CDSI.
Machine Learning (B21) - Winter 2019
This course was taught in a weekly format with 2 hours of instruction per session, in collaboration with McGill’s Building21.
Lesson 0: Welcome! (Types and values worksheet) (slides)
Lesson 1: K nearest neighbours (slides) (Types and values recap) (Data structures recap) (KNN worksheet) (KNN interactive web site) (KNN Jupyter Notebook)
Lesson 2: Decision trees (slides) (Decision trees notebook) (Decision tree visualization)
Lesson 3: Neural networks (slides) (Neural network notebook) (Neural network POGIL)
Lesson 4: Neural networks, and coding lab (slides) (lab resources)
Lesson 5: Unsupervised learning and ML ethics (slides) (lab resources) (K-means POGIL)
Software (B21) - Fall 2018
This course was taught in a weekly format with 2 hours of instruction per session, in collaboration with McGill’s Building21.
Lesson 0: Welcome! (slides)
Lesson 1: K nearest neighbours (slides)
Lesson 2: Decision trees (slides)
Lesson 3: Neural networks. (slides)
Lesson 4: Applications of machine learning and coding lab (slides)
Lesson 5: Unsupervised learning and ethical problems of machine learning. (slides)
Hardware (B21) - Fall 2018
This course was taught in a weekly format with 2 hours of instruction per session, in collaboration with McGill’s Building21.
Lesson 0: Welcome! (slides)
Lesson 1: Binary and logic (slides)
Lesson 2: Integrated circuits, and the ALU (slides)
Lesson 3: Memory and storage (slides)
Lesson 4: Processor and machine code (slides)
Lesson 5: Operating systems (slides)
Computing Workshop (B21) - Fall 2017
This course was taught in a weekly format with 2 hours of instruction per session, in collaboration with McGill’s Building21.
Lesson 8: Remedial web development work (no slides or lesson plan)