Understanding Lecture 9 28 Sep Cpsc 340 2020w Machine Learning And Data Mining
If you are looking for information about Lecture 9 28 Sep Cpsc 340 2020w Machine Learning And Data Mining, you have come to the right place. More clustering, DBSCAN (video, demo), Hierarchical Clustering, Phylogenetic Trees https://www.cs.ubc.ca/~fwood/CS340/
Key Takeaways about Lecture 9 28 Sep Cpsc 340 2020w Machine Learning And Data Mining
- Non-parametric models: K-nearest neighbors, Decision Theory for Darts, Norms https://www.cs.ubc.ca/~fwood/CS340/
- Outlier Detection, Empirical Study https://www.cs.ubc.ca/~fwood/CS340/
- Probabilistic Classifiers: Conditional probability, Naive Bayes, Probabilities and Battleship https://www.cs.ubc.ca/~fwood/CS340/
- Deep Learning
- Convolutions.
Detailed Analysis of Lecture 9 28 Sep Cpsc 340 2020w Machine Learning And Data Mining
Principal Component Analysis, More Linear Classifiers, Support Vector More Regularization, RBF video, RBF and Regularization video.
Boosting, AdaBoost, XGBoost.
We hope this detailed breakdown of Lecture 9 28 Sep Cpsc 340 2020w Machine Learning And Data Mining was helpful.