Date Topic Notes Reading
Week 1
2023-10-30
15:00-17:00
Introduction [slides] Deep Learning Book (Ch. 1, 2, 3)
2023-10-31
10:00-12:00
Serverless Machine Learning [slides] For more details on serverless ML: https://www.serverless-ml.org/
Week 2
2023-11-06
08:00-10:00
Regression, Classification, and Decision Trees [slides] Deep Learning Book (Ch. 4, 5)
Handson Book (Ch. 2, 4)
2023-11-7
13:00-15:00
Deep Feedforward Networks [slides]
2023-11-9
8:00-10:00
Lab 1 See Canvas for details of Lab 1 and deadlines.
Week 3
2023-11-13
15:00-17:00
Training Deep Feedforward Nets Slides Deep Learning Book (Ch. 7, 8), Handson Book (Ch. 11)
2023-11-14
15:00-17:00
Stefan Krawczyk from Dagworks will talk about feature engineering with Hamilton Erik Bernhardsson will talk about Modal See Video here on KTH.
2023-11-17
8:00-10:00
Lab 1 See Canvas for details of Lab 1 and deadlines.
Week 4
2023-11-20
15:00-17:00
Convolutional Nets Slides Deep Learning Book (Ch. 9), Handson Book (Ch. 14), 10 CNN Architectures [link]
2023-11-21
10:00-12:00
RNNs, Attention and Transformers Slides Deep Learning Book (Ch. 10) Handson Book (Ch. 15, 16) Attention Is All You Need [paper]. Mechanics of Seq2seq Models With Attention [link]. The Illustrated Transformer [link]
2023-11-23
15:00-17:00
Lab 2 See Canvas for details.
Week 5
2023-11-27
08:00-10:00
Feature Stores for Machine Learning slides, Hopsworks Docs Reading references in slides.
2023-11-28
10:00-12:00
Machine Learning Operations (MLOps) slides Reading references in slides.
Week 6
2023-12-04
15:00-17:00
Real-Time Machine Learning slides Reading references in slides.
2023-12-05
10:00-12:00
Project Presentation See Canvas for Details
Week 7
2023-12-11
13:00-15:00
AutoML: Hyperparameter Tuning and Neural Architecture Search slides Reading references in slides.
2023-12-12
13:00-15:00
Data and Model Parallel Training of Deep Neural Networks slides Reading references in slides.