By Philip Pham
In an effort to learn more about machine learning, I've decided to go through the textbook Machine Learning: a Probabilistic Perspective by Kevin Murphy. I also want to learn more Python, so I've decided to write up solutions to selected exercies in Jupyter notebooks.
In general, I try to derive the mathematical results in the notebooks, too, so that the code is not completely opaque. When it comes to modeling, at times I will use an existing scikit-learn implementation, but often, I will try to implement the model myself with NumPy and SciPy when I feel that doing so is instructive. Plotting is done with matplotlib. TensorFlow is used for the neural networks.
Here's an index of some of the notebooks, which will be updated as I make more progress.
- Chapter 1: Introduction
- Chapter 3: Generative models for discrete data
- Chapter 4: Gaussian models
- Chapter 7: Linear Regression
- Chapter 8: Logistic Regression
- Chapter 12: Latent Linear Models