Schedule

Suggested Readings:

• CIML, Chapter 1
• Atri’s 440 Pipeline Walkthrough
• Quick “What is ML” Video
• Important Alternative “What is ML” Video

Suggested Readings:

• The course syllabus
• Seeing Theory, Probability/Stats Review
• Probability Cheatsheet

Suggested Readings:

• Installing Jupyterlab
• Chapter 1, Shalizi Advanced Data Analysis

Suggested Readings:

• Dr. Chandola’s Linear Regression Notes
• Hunter Schafer’s CSE/STAT 416, Ch. 1

Strongly Suggested Readings:

• CIML Chapter 2, up to 2.6
• Hunter Schafer’s CSE/STAT 416, Ch. 2

Suggested Reading

• sklearn regression tutorial

Suggested Reading

• Sebastian Raschka Model Eval Notes

Required Watching

• StatQuest, Decision Tree Regression

Suggested Reading

• CIML, 3.1-3.2, 3.5
• Reminder Lecture notes are on UBLearns

Suggested Reading/Watching:

• StatQuest on the bootstrap
• StatQuest on Ridge/Lasso
• Armando Teixeira on GAMs
• Reminder Lecture notes are on UBLearns

Suggested Readings:

• Cornell Intro ML Lecture 1
• CIML, 9.1-9.4
• Cornell Into ML Lecture 4
• Reminder Lecture notes are on UBLearns

Suggested Readings:

• CIML, Ch. 7
• Tom Mitchell’s ML Book - Naive Bayes and Logistic Regression
• Cornell Into ML Lecture 6

Required Viewing

• StatQuest on Decision Trees for Classification
• StatQuest on Random Forests
• StatQuest on AdaBoost

Suggested Readings:

• Cornell Into ML Lecture 5
• StatQuest on MLE for Logistic Regression
• L18/19 Cornell ML

Details

• The review will be Jeopardy-style
• You will participate in your PA groups
• Winners will receive prizes, but you must be present to receive your prize
• The last 20 or so minutes will be open review; please bring questions if you have them.

Required Readings

• CIML, Section 5.5

Suggested Readings

• Data-centric AI
• A python Library for Computing Krippendorf’s Alpha
• Computing Krippendorf’s Alpha

For the curious reader

• Cheap and Fast, But is it Good?
• Comparing Bayesian Models of Annotation

Required

• StatQuest K-Means

Suggested Readings:

• Bishop, Section 9.1
• Visualizing K-Means
• CIML Ch.15

Suggested:

• Jordan Boyd-Graber on GMMs
• Bishop, Section 9.3

Required:

• StatQuest, Hierarchical Clustering

Required:

• StatQuest, PCA Basics

Suggested:

• Longer StatQuest, PCA
• PCA, Visualized

Curious:

• 3Blue1Brown Eigenvectors
• UMAP

See UBLearns for relevant documents

Required:

• StatQuest, Bayes Theorem

Suggested:

• BDA, Sections 1.3-1.4
• Prob. Programming and Bayesian Methods For Hackers, Ch. 1: Example: Inferring behaviour from text-message data

Suggested:

• Cornell Intro ML, Lecture 4
• MML, Section 8.5

Required:

• StatQuest, Neural Networks
• StatQuest, Neural Nets, Multiple Input/Outputs

Suggested:

Required:

• Slides are on UBLearns
• StatQuest, ReLU
• StatQuest, Convolutional Nets

Suggested

• Chris Olah, ConvNets

Required:

• Slides are on UBLearns
• StatQuest, BackProp

Strongly Suggested

• Chris Olah, Backprop
• Sebastian Raschka, Backprop

For the Curious

• Why is training deep networks hard?

Required:

• Fair Warning, Abebe Birhane

Strongly Suggested

• Bias in NLP
• Emily Bender on the recent NYT GPT-3 article

For the Curious

• Reading/Watching/Listening List for ML&Soc UB Course
• The Stochastic Parrots paper

Required:

• Fair Warning, Abebe Birhane

For the Curious

• Calculating the Souls of Black Folk
• The Root Of Algorithmic Bias And How To Deal With It

For the Curious

• Causal Inference: The Mixtape
• DoWhy/EconML: Library for computational inference