Introduction to Lecture 6 21 Sep Cpsc 340 2020w Machine Learning And Data Mining
Exploring Lecture 6 21 Sep Cpsc 340 2020w Machine Learning And Data Mining reveals several interesting facts. Non-parametric models: K-nearest neighbors, Decision Theory for Darts, Norms https://www.cs.ubc.ca/~fwood/CS340/
Lecture 6 21 Sep Cpsc 340 2020w Machine Learning And Data Mining Comprehensive Overview
Convolutions. Feature Engineering, Gmail Priority Inbox. Linear Classifiers, Perceptron.
Exploratory
Summary & Highlights for Lecture 6 21 Sep Cpsc 340 2020w Machine Learning And Data Mining
- Gradient Descent, Convex Functions https://www.cs.ubc.ca/~fwood/CS340/
- MLE and MAP, Maximum Likelihood Estimation.
- Probabilistic Classifiers: Conditional probability, Naive Bayes, Probabilities and Battleship https://www.cs.ubc.ca/~fwood/CS340/
- Nonlinear regression - Why should one learn
- Feature Selection, Genome-Wide Association Studies.
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