In this github repo, I share materials corresponding to two courses that I teach at the University of Calgary:
- ENEL645 - Data Mining & Machine Learning
- ENSF 619.02 - Advanced Image Analysis and Machine Learning
This repo is constantly being updated. Initially, we used TensorFlow as the deep learning, but we will be adding PyTorch examples since it is the dominant deep learning framework at the moment.
- Assignment 01 due January 30th at midnight - Submit in a D2L dropbox
- Assignment 02 due March 6th at midnight - Submit in a D2L dropbox
- Midterm is on February 28th in the classroom
- Midterm accommodation for those who missed the previous date is on March 6th in the classroom
- Final Projects is due March 27th - Submit in a D2L dropbox
- Reading week 18-24 February | March 29th Good Friday | April 1st Easter Monday
Templates for final project:
Week 0
Week 1
- M: L: Course Overview
- W: L: Fundamentals ML
- F:
Week 2
Week 3
- M: Weights and Biases (T - Natalia)
- W:
- F: T: Convolutional Neural Networks - Image Classification
Week 4
- M: T: Fully Connected NN - Revisited
- W:
- F: Garbage classifier - images (T - cluster)
Week 5
- M: Garbage Classifier - Transfer Learning on Text (T - Natalia)
- W: The U-net model (L - Natalia)
- F: U-net Denoising 1D signals (T - Natalia)
Week 6
- M: Transformers (L - Peyman)
- W: Transformers (T - Peyman)
- F: Self-supervised learning (L - Peyman)
Week 7
- M: - Reading week
- W: - Reading week
- F: - Reading week
Week 8
- M: Self-supervised learning (T - Peyman)
- W: - Midterm
- F: Domain Adaptation (L)
Week 9
- M: Responsible AI (L - Mahsa)
- W: Midterm (2nd chance)
- F: Responsible AI (T - Mahsa)
Week 10
- M: XAI (L - Mahsa)
- W: XAI (T - Mahsa)
-
- F: Physics Informed NNs (L - Natalia)
Week 11
- M: Physics Informed NNs (T - Natalia)
- W: Graph NNs (L - Natalia)
- F: Graph NNs (T - Natalia)
Week 12
- M: Traditional ML
- W: (spare class if things need to be shifted)
- F:Good Friday
Week 13
- M: Easter Monday
- W: Final projects
- F: Final projects
Week 14
- M: Final projects
- Assignment 01 is due February 26th at midnight - Submit in a D2L dropbox
- Assignment 02 is a reading assignment - Dates will be decided in the first week of class.
- Quiz 01 is on February 2nd in the classroom. Bring your laptop.
- Quiz 02 is on March 1st in the classroom. Bring your laptop.
- Final Projects is due March 29th - Submit in a D2L dropbox
- Reading week 18-24 February | March 29th Good Friday | April 1st Easter Monday
Templates for final project:
Week 0
Week 1
-
M:
- Meet and Greet
- L: Course Overview
-
F:
- L: Fundamentals ML
- T: Overfitting and Regularization
- L: Overfitting and Regularization
- Define presentation dates
Week 2
- M:
- F:
Week 3
-
M:
-
F:
- L:Transfer Learning
- T: Transfer Learning
- T: Garbage classifier - images (cluster)
Week 4
-
M:
- L: The U-net model
- T: Medical image segmentation cluster
-
F:
- Quiz #01
- Paper presentations - TBD