Introduction to basic concepts of machine learning and statistical pattern recognition; techniques for classification, clustering and data representation and their theoretical analysis.
Grading will be based on:
Date | Topic | Required Reading | Suggested Reading |
---|---|---|---|
8/21 | Course Overview | Murphy Chapter 1 | Probability Primer (2.1-2.5, 3.1-3.3), Linear Algebra, Calculus |
8/23 | Decision Trees | CIML Chapter 1 | Murphy 16.2 |
8/30 | Guest Lecture (Wei Xu) | Extracting Lexically Divergent Paraphrases from Twitter | |
9/4 | Statistical Estimation | Murphy Chapter 3.1-3.4 | |
9/6 | Statistical Estimation (cont) | Murphy Chapter 3.4-3.5 | CIML Chapter 9 |
9/11 | Dirichlet-Multinomial + Naive Bayes | Murphy Chapter 3.4-3.5 | CIML Chapter 9 |
9/13 | Linear Regression | Murphy Chapter 7.1-7.3, 7.5 | 7.4, 7.6 |
9/18 | Logistic Regression | Murphy 8.1,8.2,8.31,8.32 | Tom Mitchell's Notes on NB + LR |
9/18 @ 4pm | Guest Lecture by Eunsol Choi (University of Washington) | QuAC Paper | |
9/19 @ 4:30pm | Guest Lecture by Mark Yatskar (Allen Institute for Artificial Intelligence) | Gender Bias Amplification Paper | |
9/20 | Logistic Regression | Murphy 8.3.7, 8.5, 8.6.1 | Ng & Jordan 2002 |