| MATH80629A | Lectures | Homework | Lab | Project | Office hour
Machine Learning for Large-Scale Data Analysis and Decision Making (MATH80629A): Fall 2021
Hands-on Sessions
Most of the lab materials are in Python using Colab. If you want to create a machine learning model but you don’t have a machine that can take the workload or you don’t want to deal with installing packages and resolving installation issues, Google Colab is a suitable option. Colaboratory is a free Jupyter notebook environment provided by Google where you can use free GPUs and TPUs.
Getting Started
To start working with Colab you first need to log in to your google account, then go to this link. If you are a new Colab user, you can check here to learn more.
Schedule
1- Week 1 (August 30): Class introduction and math review
- No hands-on
2- Week 2 (September 6): No class
- Holiday in Canada: Labour Day
3- Week 3 (September 13): Machine learning fundamentals
- Class summary
- Exercises (colab). If you do not want to use colab, here are the two files you need to download: 1) Fundamentals_questions.ipynb OR Fundamentals_questions.py AND 2) utilities.py
- Solution (colab)
4- Week 4 (September 20): Supervised learning algorithms
- Class summary
- Exercises (colab). If you do not want to use colab, here are the two files you need to download: 1) Fundamentals_questions.ipynb OR Fundamentals_questions.py AND 2) utils.py
- Solution (colab)
5- Week 5 (September 27): Python for scientific computations and machine learning
6- Week 6 (October 4): Neural networks and deep learning
7- Week 7 (October 11): No class
- Holiday in Canada: Thanksgiving
8- Week 8 (October 18): Recurrent Neural networks and Convolutional neural networks
- Class summary
- Exercises RNNs (colab)
- Exercises CNNs (colab)
- Solution RNNs (colab)
- Solution CNNs (colab)
- Optional: if you are intrested to learn more, this Exercises CNNs: Pytorch (colab) is an example of CNNs implemented in Pytorch.
9- Week 9 (October 25): Project meetings
- No hands-on
10- Week 10 (November 1): Unsupervised learning
11- Week 11 (November 8): Parallel computational paradigms for large-scale data processing
12- Week 12 (November 15): Trustworthy Machine Learning
13- Week 13 (November 22): Sequential decision making I
14- Week 14 (November 29): Sequential decision making II
15- Week 15 (December 6): Class Project presentation
- Room: Manuvie
16- Week 16 (December 13)
- December 16 (9 am - 12 pm): Final exam