Introduction to Machine Learning
1.
Week 1 - Data, Numpy, Matrices, Error/Loss functions and Regression
2.
Week 2 - Non-Linear Regression, OLS, and Log Loss
3.
Week 3 - Classification: SVMs, Naive Bayes, KNN and Decision Trees
4.
Week 4 - Classification & Intro to Unsupervised Learning: Clustering & Dimensional Reduction
5.
Week 5 - Neural Networks: ANNs, DNNs, and CNNs
6.
Jupyter Notebook Export Tutorial
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UCSD CSE151A Summer 2025
Week 3 - Classification: SVMs, Naive Bayes, KNN and Decision Trees
Week 3 Lecture Material
Lecture Slides
Slides PDF SVMs
Slides PDF SVM Optimization
Slides PDF Naive Bayes
Slides PDF Decision Trees
Slides PDF K-Nearest Neighbors
Classification Loss Functions
Slides PDF KNNs
Notebooks
SVM Notebook
Linear SVM Starter Code
BCC Data Notebook
Decision Tree Notebook
Decision Tree Scratch Notebook
Worksheets
Blank Participation Worksheet
Week 3 Discussion
Notebook