This is where I display grades and organize resources for SIE598/DSE501 — Statistical Foundations of Data Science


Fall 2022



Interesting links:

• Textbook Website (WEKA)


• Download WEKA Explorer (Java)



• Holte's 1-R Paper (1993)



• Data Derby


• UCI Machine Learning DataSet Archive


• Our world in Data
• 2019: Best Year Ever


• AI tutorials


• ANN in Plain English


• fivethirtyeight.com: Blog on Politics, Sports, Science & Culture


• How Machines Learn (CGP Grey)
• How Machines *REALLY* Learn (CGP Grey)
• No Humans Need Apply (CGP Grey)
• You Are Two (CGP Grey)



• Matchbox TicTacToe


• Smart Rockets



• Useful StatQuest (and other) YT videos for ANN
  • Stochastic Gradient Descent - StatQuest
  • Gradient Descent - MIT
  • Stochastic Gradient Descent - 3Blue1Brown ANN series


• So. Many. ANN Videos!
  
  • Neural Networks From Scratch - Single-stop ANN Explainer
  
  
  • Neural Networks Demystified - ANN Explainer Series
  
  
  • 'Neural Networks From Scratch in Python' Series - end-to-end Python coding of an ANN (using Numpy library)
  
  



• Web-scraping application: Data-mining OKCupid and Clustering People — for Love
• Chris McKinlay - I Hacked OkCupid (YouTube)
• Chris McKinlay: The Best Dating Website? (YouTube)
• Chris McKinlay Lightning Talk: Hacking OkCupid (YouTube)


• And now the other side: Data-mining an online relationship — with a catfish (featuring GoogleSheets!)
• Data-mining Sorries






• How demographics predict party affiliation (NYT)
• What Unites Republicans May Be Changing. Same With Democrats. (538)


• Predicting Recidivism: Mechanical Turkers paid $1 are better at predicting recidivism than secret, private-sector algorithms


• How Likes May Have Helped Trump Win: Psychometric data-mining and the 2016 election


• How an algorithm helped a global tennis match-fixing investigation


• The NSA's SKYNET program may be killing thousands of innocent people


• Data Science Ethics article (Slate.com)


DS12 Data Science Lectures (feat. Chris McKinley)

• Introduction to Machine Learning (YT)
• Linear Regression (YT)
• Bayesian Inference (YT)
• Matrix Decomposition (YT)



• Introduction to Data Mining YT Series (using R)


• Machine Learning Foundations: Ep #1 - What is ML?

Homework Assignments:

• Final Presentation: Dataset Analysis and Presentation of Results Due Fri 12/15



• HW4: The Decision Tree model Due Fri 11/18



• HW3: The Naive Bayes Classifier model Due Fri 11/04



• EC1: HW1/2 Clarity Proposals Due Fri 10/14



• HW2: The Limits of Linear Regression Due Fri 10/07



• HW1: Exploration of 3x3 Pattern Classification via Regression — Due Thurs 9/22