Schedule (Section 0201)

Subject to change.

Date Topics Readings
Th Jan 25 Welcome to Machine Learning (slides) math4ml
Tu Jan 30 Decision Trees (slides) CIML 1 + Syllabus
Th Feb 1 Limits of learning; Overfitting/Underfitting (slides) CIML 2
Tu Feb 6 Geometry and nearest neighbors (slides) CIML 3-3.3
Th Feb 8 K-means clustering (slides) CIML 3.4-3.5
Tu Feb 13 Perceptron I (slides) CIML 4-4.5+ NumPy for MATLAB users
Tu Feb 20 Perceptron II (slides) CIML 4.5-4.7
Th Feb 22 Practical Issues (slides) CIML 5-5.5
Tu Feb 27 Learning from Imbalanced Data (slides) CIML 6.1
Th Mar 1 Multiclass Classification (slides) CIML 6.2-6.3
Tu Mar 6 Review and Practice Problems
Th Mar 8 Midterm
Tu Mar 13 Bias and Fairness (slides) CIML 8
Th Mar 15 Linear models, gradient descent (slides) CIML 7-7.4
Spring Break!
Tu Mar 27 Gradient Descent and Subgradient Descent (slides) CIML7.4 - 7.7
Th Mar 29 Conditional Models (slides) CIML9-9.5
Tu Apr 3 Naive Bayes (slides) CIML9.6-9.7
Th Apr 5 PCA (slides) CIML15.2
Tu Apr 10 Practice Problems
Th Apr 12 Neural Networks I (slides) CIML10-10.3
Tu Apr 17 Neural Networks II (slides) CIML10.4-10.6
Th Apr 19 Deep Learning I (slides)
Tu Apr 24 Deep Learning II (slides)
Th Apr 26 Kernels (slides) CIML 11-11.3
Tu May 1 SVMs I (slides) CIML11.4-11.6
Th May 3 SVMs II (slides) CIML 15-15.1
Tu May 8 Review and Perspectives
Th May 10 Practice Problems and T/F Problems
Sat May 12 Final Exam 8:00 - 10:00 am

Web Accessibility