Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• Introduction
  tl;dr:
  [Slides] [Lab] [Colab tutorial]
  

  Suggested Readings:

  • HMST Chapter 1 and Chapter 2
  • [Recorded video]
• Neural network and its training
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 10 and Chapter 11
  • [Recorded video for lecture]
  • [Recorded video for lab]
• Convolutional Neural Networks
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 14
  • [Recorded video for lecture]
  • [Recorded video for lab (second part)]
• Recurrent Neural Networks
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 15 and Chapter 16
  • [Recorded video for lecture]
  • [Recorded video for lab]
• Representation learning
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 17
  • [Recorded video]
• Transfer learning
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 11 and Chpater 14
  • [mnist-m dataset]
  • [Recorded video]
• Hyperparamter search and meta learning
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 10
  • [Recorded video]
• Spring break (No class)
  tl;dr:
  
  

  Suggested Readings:

  • HMST Chapter 13
• Midterm Porject (No class)
  tl;dr:
  
  

  • Deep learning with Python, Second Edition Chpater 14
• Framing the problem and constructing the dataset
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 1
  • [Recorded video]
• Database and Data Wrangling
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 1
  • [Recorded video]
• Data cleaning and feature engineering
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 1
  • [dataset for lab]
  • [Recorded video]
• Dimensional reduction and clustering
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 8,9
  • [dataset for lab]
  • [Recorded video]
• Explainable AI
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • Interpretable Machine Learning
  • [dataset for lab]
  • [Recorded video]
• Model serving
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 1, 19
  • [Recorded video]
• Gradient boosting and ensemble learning
  tl;dr:
  [Slides] [Lab]
  

  Suggested Readings:

  • HMST Chapter 6, 7
  • [dataset for lab]
  • [Recorded video]
• Final Project Presentation I
  tl;dr:
  
  

  Suggested Readings:

• Final Project Presentation II
  tl;dr:
  
  

  Suggested Readings: