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 2 - Non-Linear Regression, OLS, and Log Loss
Week 2 Lecture Material
Lecture Slides
Slides PDF Ordinary Least Squares Optimization Method
Slides PDF Gradient Descent
Slides PDF Data Preprocessing
Slides PDF Polynomial
Slides PDF Logistic Regression
Slides PDF HPC/SDSC
Notebooks
Gradient Descent Notebook
BCC Data Notebook
Polynomial Regression Notebook
Processing California Housing Notebook
Normality Testing Using Diabetes Data Notebook
Worksheets
Blank Participation Worksheet
Week 2 Discussion TBA
Slides
Notebook