MSU AI MicroBootCamp Notes and Projects

Module 0: Getting Started

Getting Started

0.1.1: Welcome
0.1.2: Course Tools
0.1.3: Google Colab
0.1.4: Local Installations

Navigating the Course

0.2.1: Course Overview
0.2.2: Course Structure
0.2.3: Submitting Assignments
0.2.4: Grade Notifications
0.2.5: Your Support Team

Module 1: Introduction to AI

Module 1 Notes

Introduction to Artificial Intelligence

1.1.1: Welcome to the AI Micro Boot Camp!
1.1.2: What is AI?
1.1.3: Narrow AI vs Artificial General Intelligence
1.1.4: Ethics and AI
1.1.5: Recap and Knowledge Check

The Impact of Machine Learning

1.2.2: Business
1.2.3: Medicine
1.2.4: Daily Life
1.2.5: Recap and Knowledge Check

Machine Learning Models and Methods

1.3.1: Overview of Machine Learning Models
1.3.2: Unsupervised Learning
1.3.3: Supervised Learning
1.3.4: Machine Learning Optimization
1.3.5: Neural Networks and Deep Learning
1.3.6: Natural Language Processing (NLP) and Transformers
1.3.7: Emerging Technologies
1.3.8: Recap and Knowledge Check
1.3.9: References

Module 2: Unsupervised Learning

Module 2 Notes

Introduction to Unsupervised Learning

2.1.1: What is Unsupervised Learning?
2.1.2: Recap
2.1.3: Getting Started
2.1.4: Clustering
2.1.5: Recap and Knowledge Check
2.1.6: Segmenting Data: The K-Means Algorithm
2.1.7: Using the K-Means Algorithm for Customer Segmentation
2.1.8: Activity: Spending Beyond Your K-Means
2.1.9: Recap and Knowledge Check

Optimizing Unsupervised Learning

2.2.1: Finding Optimal Clusters: The Elbow Method
2.2.2: Apply the Elbow Method
2.2.3: Activity: Finding the Best k
2.2.4: Recap and Knowledge Check
2.2.5: Scaling and Transforming for Optimization
2.2.6: Apply Standard Scaling
2.2.7: Activity: Standardizing Stock Data
2.2.8: Recap and Knowledge Check

Principal Component Analysis

2.3.1: Introduction to Principal Component Analysis
2.3.2: Recap and Knowledge Check
2.3.3: Activity: Energize Your Stock Clustering
2.3.4: Recap and Knowledge Check

Summary: Unsupervised Learning

2.4.1: Summary: Unsupervised Learning
2.4.2: Reflect on Your Learning
2.4.3: References

Module 3: Supervised Learning — Linear Regression

Module 3 Notes

Supervised Learning Overview

3.1.1: Introduction to Supervised Learning
3.1.2: Supervised vs. Unsupervised Learning
3.1.3: Getting Started

Supervised Learning Key Concepts

3.2.1: Features and Labels
3.2.2: Regression vs. Classification
3.2.3: Model-Fit-Predict
3.2.4: Model Evaluation
3.2.5: Training and Testing Data
3.2.6: Recap and Knowledge Check

Linear Regression

3.3.1: Introduction to Linear Regression
3.3.2: Making Predictions with Linear Regression
3.3.3: Model Evaluation: Quantifying Regression
3.3.4: Activity: Predicting Sales with Linear Regression
3.3.5: Recap and Knowledge Check

Summary: Supervised Learning — Linear Regression

3.4.1: Summary: Supervised Learning — Linear Regression
3.4.2: Reflect on Your Learning
3.4.3: References

Module 4: Supervised Learning — Classification

Module 4 Notes

Classification Overview

4.1.1: Classification Overview
4.1.2: Getting Started

Classification and Logistic Regression

4.2.1: Overview
4.2.2: Understand the Categorical Data Before Applying Logistic Regression
4.2.3: Preprocessing
4.2.4: Training and Validation
4.2.5: Prediction
4.2.6: Activity: Logistic Regression
4.2.7: Recap and Knowledge Check

Model Selection

4.3.1: Introduction to Model Selection
4.3.2: Linear vs. Non-linear Data
4.3.3: Linear Models
4.3.4: Demonstration: Predict Occupancy with SVM
4.3.5: Non-linear Models
4.3.6: Demonstration: Decision Trees
4.3.7: Overfitting
4.3.8: Recap and Knowledge Check

Ensemble Learning

4.4.1: Introduction to Ensemble Learning
4.4.2: Random Forest
4.4.3: Demonstration: Random Forest
4.4.4: Boosting
4.4.5: Recap and Knowledge Check

Summary: Supervised Learning — Classification

4.5.1: Summary: Supervised Learning — Classification
4.5.2: Reflect on Your Learning
4.5.3: References

Project 1: Developing a Spam Detection Model

Project 1 Scope
Project 1 Starter Code
Project 1 Submission .ipynb format
Project 1 Submission .py format

Module 5: Machine Learning Optimization

Module 5 Notes

Introduction to Machine Learning Optimization

5.1.1: Introduction to Machine Learning Optimization
5.1.2: Getting Started

Evaluating Model Performance

5.2.1: What is a good model?
5.2.2: Overfitting and Underfitting
5.2.3: Confusion Matrix
5.2.4: Accuracy
5.2.5: Other Metrics
5.2.6: Classification Report
5.2.7: The Importance of Metric and Target Selection
5.2.8: Recap and Knowledge Check

Case Study: Imbalanced Data

5.3.1: Introduction to Imbalanced Data
5.3.2: Oversampling and Undersampling
5.3.3: Applying Random Sampling Techniques
5.3.4: Synthetic Resampling
5.3.5: Balanced Models
5.3.6: Activity: Improving Bank Marketing Campaigns with Synthetic Sampling
5.3.7: Recap and Knowledge Check

Tuning

5.4.1: Eyes on the Prize
5.4.2: Hyperparameter Tuning
5.4.3: Activity: Hyperparameter Tuning
5.4.4: How Much is Enough?
5.4.5: Realities and Limitations

Summary: Machine Learning Optimization

5.5.1: Summary: Machine Learning Optimization
5.5.2: Reflect on Your Learning
5.5.3: References

Module 6: Neural Networks and Deep Learning

Module 6 Notes

Introduction to Neural Networks and Deep Learning

6.1.1: Introduction to Neural Networks and Deep Learning
6.1.2: Your Neural Network and Deep Learning Background
6.1.3: Getting Started

Artificial Neural Networks

6.2.1: What is a Neural Network?
6.2.2: Making an Artificial Brain
6.2.3: The Structure of a Neural Network
6.2.4: Recap and Knowledge Check

Make Predictions with a Neural Network Model

6.3.1: Create a Neural Network
6.3.2: Creating a Neural Network Model Using Keras
6.3.3: Compile a Neural Network
6.3.4: Train a Neural Network
6.3.5: Make Predictions with a Neural Network
6.3.6: Activity: Predict Credit Card Defaults
6.3.7: Recap and Knowledge Check

Deep Learning

6.4.1: What is Deep Learning?
6.4.2: Predict Wine Quality with Deep Learning
6.4.3: Create a Deep Learning Model
6.4.4: Train a Deep Learning Model
6.4.5: Test and Evaluate a Deep Learning Model
6.4.6: Save and Load a Deep Learning Model
6.4.7: Activity: Detecting Myopia
6.4.8: Recap and Knowledge Check

Summary: Neural Networks and Deep Learning

6.5.1: Summary: Neural Networks and Deep Learning
6.5.2: Reflect on Your Learning
6.5.3: References

Project 2: Predict Student Loan Repayment with Deep Learning

Project 2 Scope
Project 2 Starter Code
Project 2 Submission .ipynb format
Project 2 Submission .py format

Module 7: Natural Language Processing

Module 7 Notes

Introduction Natural Language Processing

7.1.1: Introduction to NLP
7.1.2: Getting Started
7.1.3: What is Text?
7.1.4: Bag-of-Words Model
7.1.5: What is a Language Model?

Tokenizers

7.2.1: Introduction to Tokenizers
7.2.2: Tokenizer Example: Index Encoder
7.2.3: Introduction to Hugging Face Tokenizers
7.2.4: Similarity Measures
7.2.5: Tokenizer Case Study: AI Search Engine
7.2.6: Recap and Knowledge Check

Transformers

7.3.1: Introduction to Transformers
7.3.2: Pre-trained models
7.3.3: Language Translation
7.3.4: Hugging Face Pipelines
7.3.5: Text Generation
7.3.6: Question and Answering
7.3.7: Text Summarization
7.3.8: Recap and Knowledge Check

AI Applications

7.4.1: AI Applications with Gradio
7.4.2: Gradio Interfaces
7.4.3: Gradio App: Text Summarization
7.4.4: Other Gradio Components
7.4.5: Activity: Question and Answering Textbox
7.4.6: Introduction to Hugging Face Spaces
7.4.7: Recap and Knowledge Check
7.4.8: References

Module 8: Emerging Topics in AI

Module 8 Notes

Introduction to Emerging Topics in AI

8.1.1: Introduction
8.1.2: Getting Started

AI the Creator

8.2.1: Introduction
8.2.2: Interactive Text Generation
8.2.3: Image Generation
8.2.4: Music Generation
8.2.5: Problems and Possibilities
8.2.6: References

AI Outside the Computer

8.3.1: Introduction
8.3.2: Autonomous Vehicles
8.3.3: Robots
8.3.4: The Internet of Things
8.3.5: Mobile Deployment
8.3.6: Problems and Possibilities
8.3.7: References

Additional Areas of Active Research

8.4.1: Introduction
8.4.2: One-Shot Learning
8.4.3: Creating 3D Environments
8.4.4: Algorithm Speed and Computational Resource Management
8.4.5: Ethics and Regulations
8.4.6: Problems and Possibilities
8.4.7: References

Course Wrap-Up

8.5.1: Congratulations!