Lecture Slides and Notes
Topic 1: Course Introduction
[PDF]
Topic 2: Review of Linear Algebra, Probability, and Statistics
[PDF]
Topic 3: Supervised Learning: Classification and Regression
[PDF]
Topic 4: KNN Classifier, Curse of Dimensionality
[PDF]
Topic 5: K-means and K-modes Clustering
[PDF]
,
Note on density estimation
Topic 6: Gaussian Mixture Model and EM
[PDF]
,
Note on GMM
Topic 7: Graph Clustering
[PDF]
Topic 8: Dimensionality Reduction
[PDF]
,
Note
Topic 9: Neural Network
[PDF]
Topic 10: Back Propagation
[PDF]
Midterm review
[PDF]
Topic 11: Training Neural Networks
[PDF]
Topic 12: Convolutional Neural Networks
[PDF]
Topic 13: RNN and Transformers
[PDF]
Topic 14: Decision Tree
[PDF]
,
Note on decision tree
Topic 15: Ensemble Models
[PDF]
,
Note on ensemble model
Topic 16: Support Vector Machines
[PDF]
Final review
[PDF]
Credit to Prof. Miguel Carreira-Perpiñán for the notes that were used for CSE 176 at UC Merced.