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Program

7/16/2021

  • 9:40-10:00: Opening

  • 10:00-12:30: Machine learning basics I

    1. Data preprocessing and ML flow

    2. Linear regression

  • 12:30-13:30: Lunch break

  • 13:30-15:30: Machine learning basics II

    1. Logistic regression

    2. Support vector machine

    3. Tree-based models

  • 15:20-15:40: Break

  • 15:40-17:00: Hands-on practice

  • 17:00-17:30: Free discussion

7/17/2021

  • 9:40-10:00: Opening

  • 10:00-12:30: Practical deep learning

    1. Basic structure

    2. How to train an NN?

    3. Hyper-parameter tuning

  • 12:30-13:30: Lunch break

  • 13:30-15:30: Convolutional neural networks

  • 15:20-15:40: Break

  • 15:40-17:00: Hands-on practice

  • 17:00-17:30: Free discussion

7/18/2021

  • 9:40-10:00: Opening

  • 10:00-12:30: Unsupervised learning

    1. Principal component analysis

    2. Clustering

    3. Autoencoder

  • 12:30-13:30: Lunch break

  • 13:30-15:30: Kaggle competition: DL for phase transitions

  • 15:20-15:40: Break

  • 15:40-17:00: Kaggle competition: DL for phase transitions

  • 17:00-17:30: Closing