Web Technology
Syllabus
Tentative Schedule
Course content
1: Internet & WWW
Course content
1: Internet & WWW
2: Data & Features
3: Supervised Learning
4: K-Nearest Neighbors (KNN) Algorithm
5: Bias-Variance Tradeoff
6: Linear Regression
7: Optimization for ML
8: Gradient Descent Algorithm
9: Gradient Descent Algorithm
10: Logistic Regression
11: Support Vector Machines (SVM)
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Introduction to Machine Learning
Content for Monday, July 8, 2024
Slides
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Readings
Chapter 1 in
Machine Learning
(
Mitchell 1997
)
References
Mitchell, Tom M. 1997.
Machine Learning
. Vol. 1. 9. McGraw-hill New York.
2: Data & Features