Schedule

Subject to change.

Date Topics Readings
Tu Jan 26 Snow day, no class.
Th Jan 28 Welcome to Machine Learning!
Tu Feb 2 Decision Trees CIML 1-1.6 + Syllabus
Th Feb 4 Dealing with data, underfitting/overfitting CIML 1.7-1.10
Tu Feb 9 Geometry and Nearest Neighbors CIML 2-2.3
Th Feb 11 K-Means Clustering CIML 2.4-2.6
Tu Feb 16 Perceptron I CIML 3-3.5
Th Feb 18 Perceptron II CIML 3.5-3.7
Tu Feb 23 Read about practical issues in ML CIML 4
Th Feb 25 Dealing with Imbalanced Data
Tu Mar 1 Beyond binary classification I CIML 5.1-5.2
Th Mar 3 Beyond binary classification II CIML 5.3-5.5
Tu Mar 8 Linear models CIML 6-6.4
Th Mar 10 Gradient Descent
Enjoy your Spring break!
Tu Mar 22 Practice Problems Discussion
Th Mar 24 Midterm Exam
Tu Mar 29 Probabilistic Models I CIML 7-7.5
Th Mar 31 Probabilistic Models II CIML 7.6-7.7
Tu Apr 5 Unsupervised Learning I (K-Means revisited) CIML 13.1
Th Apr 7 Unsupervised Learning II (PCA) CIML 13.2
Tu Apr 12 Neural Networks I CIML 8-8.3
Th Apr 14 Neural Networks II CIML 8.4-8.6
Tu Apr 19 Kernels CIML 9-9.3
Th Apr 21 Support Vector Machines I CIML 6.5-6.7
Tu Apr 26 Support Vector Machines II CIML 9.4-9.6
Th Apr 28 Ensemble Learning CIML 11
Tu May 3 Work on P3
Th May 5 Review
Tu May 10 Review
Mo May 16 Final Exam (10:30am-12:30pm)

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