Slides

Lecture 0 : Introduction

Lecture 1_1 : Multivariate Linear Regression; Regularization

Lecture_1_2 : Logistic regression

Lecture_1_3 : Support vector machine

Lecture_2_1 : Principle components analysis
Lecture_2_2 : Recommender System; Clustering (Video, pwd:Y72A1$?P)
Lecture_3_1 : Neural Network I (Video, pwd:f!AL2mc5)
Lecture_3_2: Neural Network II
Lecture_3_3: Neural Network III

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

Support Vector Machine

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