Introduction to Machine Learning
CMSC422
University of Maryland
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Syllabus
Topics
Schedule (Section 0101)
Schedule (Section 0201)
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
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