Tweet Classification for Disaster Relief

This is the homepage for the AI4ALL 2020 NLP research project. Here you can find links to all class materials used for the research project.

2020 Instructors: Elias Wang ([email protected]) & Marion Lepert ([email protected])

Slides

• Week 1
  • Monday: Introduction to Artificial Intelligence
  • Tuesday: Introduction to Machine Learning
  • Wednesday: Introduction to NLP
  • Thursday: Data and Bias
  • Friday: Clustering
• Week 2
  • Monday: Bayes' Rule
  • Tuesday: Naive Bayes
  • Wednesday: Evaluation Metrics
  • Thursday: Word processing
  • Friday: Regression
• Week 3
  • Monday: Classification
  • Tuesday: Artificial Neural Networks
  • Wednesday: More NLP
  • Thursday: Wrap up and presentation prep
  • Friday: Presentations!

Activity Notebooks

• Python part 1
• Python part 2
• Python part 3
• Python part 4
• Rule-based Classifier
• Bias
• Clustering
• Conditional Probability Exercises Part 1
• Conditional Probability Exercises Part 2
• Naive Bayes
• Evaluation
• Language Model
• Regression
• Classification
• Preprocessing
• Visualization

Solution to Activity Notebooks

• Clustering
• Conditional Probability Exercises Part 2
• Naive Bayes
• Evaluation
• Language Model
• Regression
• Classification
• Preprocessing
• Visualization

In-class exercises

• Precision/Recall/F1 exercises

Feedback

• Please use this form to share any anonymous feedback, thoughts, or concerns with Marion and Eli.

Other materials

• Lesson 0: Data exploration spreadsheet
• Our NLP playground has interactive material to peruse for fun
• Lecture on text processing (e.g. regular expression, tokenization, lemmatization/stemming) from Stanford CS 124 by Professor Dan Jurafsky
• Unix for Poets has more details on text processing
• Python cheat sheet: feel free put comments / things you'd like to know about in the slides!
• Naive Bayes cheat sheet
• Latex to make our slides / poster pretty
• Next Steps: Resources for after AI4ALL