## Current Courses

### 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

## Past Courses

### 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

Lesson 8: Remedial web development work (no slides or lesson plan)