Slides
Chapter 1.1 Chapter 1.2 Chapter 1.3
Chapter 3.1 Chapter 3.2 Chapter 3.3
Notebooks
Multivariate Linear Regression
| Real life examples |  Run in Colab |
| To model and reveal the force of gravity | Run in Colab |
| 2D function fitting | Run in Colab |
| Gradient Descent & Conjugate Gradient | Run in Colab |
Logistic Regression
| Real life examples | Run in Colab |
| Example on grade weighting | Run in Colab |
| Fisher's Iris | Run in Colab |
Support Vector Machine
| Fermi surface | Run in Colab |
| Real life examples | Run in Colab |
| Fisher's Iris | Run in Colab |
Principal Component Analysis
| Real life examples | Run in Colab |
| Ising model | Run in Colab |
Clustering
| Real life examples | Run in Colab |
| Ising model | Run in Colab |
Neural Network
| Perceptron | Run in Colab |
| Neural network in Fisher's Iris classification | Run in Colab |
| Back propagation_wheat-seeds | Run in Colab |
| MNielsen network | Run in Colab |
| Feedforward Neural Network | Run in Colab |
Supplemental Materials
| 1. Handwritten notes on SVM | SVM |
| 2. Mean field hubbard model on square lattice An example on solving ground state energy using gradient descent method |
Run in Colab |
| 3. Handwritten notes on 2D Ising model | Ising model |
| 4. Handwritten notes on Neural Network | Feed-forward&Back-propagation |