# Slides

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

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 latticeAn 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 |