Introduction to Machine Learning
1.
Week 1,2 - Data, Numpy, Matrices, Error/Loss functions and Regression
2.
Week 3,4 - Non-Linear Regression, OLS, and Log Loss
3.
Week 5,6 - Classification: SVMs, Naive Bayes, KNN and Decision Trees
4.
Week 7,8 - Classification & Intro to Unsupervised Learning: Clustering & Dimensional Reduction
5.
Week 9,10 - Neural Networks: ANNs, DNNs, and CNNs
6.
Jupyter Notebook Export Tutorial
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UCSD CSE151A Fall 2024
Week 5,6 - Classification: SVMs, Naive Bayes, KNN and Decision Trees
Week 5,6 Lecture Material
Lecture Slides
Slides PDF Gradient Descent
Slides PDF SVMs
Slides PDF Naive Bayes
Slides PDF Decision Trees
Notebooks
Gradient Descent Notebook
Decision Tree Notebook
BCC Data Notebook
SVM Notebook
Decision Tree Scratch Notebook
Week 5 Discussion
Slides
Notebook
Week 6 Discussion
Slides
Notebook