Welcome to the Mitsubishi AI Workshop materials repository! This repository contains all the resources, including slides and Jupyter notebooks, needed for the three-day workshop. Each day of the workshop focuses on distinct topics with corresponding lectures and interactive lab sessions.
If you have questions, please email Alex: alex.olson@utoronto.ca.
Lecture
| Time | Topic | Speaker | Slides | Recording |
|---|---|---|---|---|
| 09:00 – 10:00 | Foundations of AI and Machine Learning | Alex Olson | Slides: Foundations of AI and Machine Learning | Link to recording |
Interactive Labs
| Time | Topic | Lab Notebook | Recording | Answered Notebook | |
|---|---|---|---|---|---|
| 10:15 – 12:00 | Exploratory Data Analysis (EDA) with Python | Lab Notebook: Intro to Python | Link to recording | Answered Notebook: Intro to Python | PDF: Intro to Python |
| Data Cleaning and Processing | Lab Notebook: Data Cleaning and Processing | Link to recording | Answered Notebook: Data Cleaning and Processing | PDF: Data Cleaning and Processing |
Lecture
| Time | Topic | Speaker | Slides | Recording |
|---|---|---|---|---|
| 13:00 – 14:00 | Introduction to Neural Networks | Alex Olson | Slides: Neural Networks | Link to recording |
Links
| Topic | Link |
|---|---|
| 3Blue1Brown: But What is a Neural Network? | Link to video |
| TensorFlow Neural Network Playground | Link to playground |
| CNN Visualization | Link to visualization |
Interactive Lab
| Time | Topic | Lab Notebook | Answered Notebook | |
|---|---|---|---|---|
| 14:15 – 16:00 | Decision Trees and Neural Networks | Lab Notebook: Decision Trees and Neural Networks | Answered Notebook: Decision Trees and Neural Networks | PDF: Decision Trees and Neural Networks |
Interactive Labs
| Time | Topic | Lab Notebook | Answered Notebook | |
|---|---|---|---|---|
| 09:00 - 10:45 | Zero-Shot Classification with CLIP | Lab Notebook: Zero-Shot Classification | Answered Notebook: Zero-Shot Classification | PDF: Zero-Shot Classification |
| 09:00 – 10:45 | Visual Inspection with CNNs | Lab Notebook: Computer Vision | Answered Notebook: Computer Vision | PDF: Computer Vision |
Guest Lecture
| Time | Topic | Speaker | Slides |
|---|---|---|---|
| 11:00 – 12:00 | Deep Learning Applications in Engineering Asset Management | Professor Chi-Guhn Lee | Slides provided during the session |
Interactive Labs
| Time | Topic | Lab Notebook | Answered Notebook | |
|---|---|---|---|---|
| 13:00 – 14:45 | Sentiment Analysis with LLMs | Lab Notebook: Intent Classification | Answered Notebook: Intent Classification | PDF: Intent Classification |
Guest Lecture
| Time | Topic | Speaker | Slides |
|---|---|---|---|
| 15:00 – 16:00 | Practical Aspects of Machine Learning and Recommendation Systems | Professor Scott Sanner | Slides provided during the session |
Lecture
| Time | Topic | Speaker | Slides | Recording |
|---|---|---|---|---|
| 09:00 – 10:00 | Optimization Techniques in Operations | Alex Olson | Slides: Optimization | Link to recording |
Links
| Topic | Link |
|---|---|
| Gradient Descent Visualizer | Link to visualizer |
Interactive Labs
| Time | Topic | Lab Notebook | Answered Notebook | |
|---|---|---|---|---|
| 10:15 – 12:00 | Linear Programming | Lab Notebook: Linear Programming | Answered Notebook: Linear Programming | PDF: Linear Programming |
| Supply Chain Optimization | Lab Notebook: Optimization | Answered Notebook: Optimization | PDF: Optimization | |
| Scheduling | Lab Notebook: Scheduling | Answered Notebook: Scheduling | PDF: Scheduling |
Lecture
| Time | Topic | Speaker | Slides | Recording |
|---|---|---|---|---|
| 13:00 – 14:00 | Deployment Considerations | Alex Olson | Slides: Deployment Considerations | Link to recording |
Instructions for Your Group:
Case Studies
slides folder. Each slide set corresponds to a specific lecture.For any questions or issues, please open an issue in this repository or contact the workshop organizers.