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 |
---|---|---|---|
1/10 | Course Overview | Murphy Chapter 1 | Probability Primer (2.1-2.5, 3.1-3.3), Linear Algebra, Calculus |
1/12 | Decision Trees | Murphy 16.2 | CIML Chapter 1 |
1/17 | Decision Trees (cont) and Probability Review | Murphy Chapter 2.1-2.5, 2.8 | Mackay Book 2.1-2.3 |
1/19 | Statistical Estimation | Murphy Chapter 3.1-3.4 | |
1/22 | Dirichlet-Multinomial + Naive Bayes | Murphy Chapter 3.4-3.5 | |
1/24 | Linear Regression | Murphy Chapter 7.1-7.3, 7.5 | 7.4, 7.6 |
1/31 | Logistic Regression | Murphy 8.1,8.2,8.31,8.32 | Tom Mitchell's Notes on NB + LR |
2/2 | Logistic Regression | Murphy 8.3.7, 8.5, 8.6.1 | Ng & Jordan 2002 |
2/7 | Perceptron | CIML Chapter 4 | |
2/9 | Instance-Based Learning | CIML Chapter 3 | |
2/14 | Kernel Methods | CIML Chapter 11 | |
2/16 | SVMs | CIML Chapter 7 | Murphy 14.1, 14.2, 14.3, 14.4, 14.5 |
2/21 | Neural Networks | Murphy 16.5 | CIML Chapter 10, Deep Learning Book Chapter 6 |
2/23 | Neural Networks | Murphy 16.5 | CIML Chapter 10, Deep Learning Book Chapter 6 |