You can download the lectures here. Big Thanks to Jordan Sanders for agreeing to take notes in class that I can post here.

  • Lecture 1 Course Introduction
    Description: We will go over course policies, a brief description and introduction
    [Notes]
  • Lecture 2 Singular Value Decomposition (SVD)
    Description: We will go over basics of SVD
    [Notes]

    Suggested Readings:

  • Lecture 3 Image Compression with SVD
    Description: We will explore image compression in python using SVD
    [Notes]

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  • Lecture 4 Image Encoding
    Description: Here we will look at computation of bases matrix for face images
    [Notes]

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  • Lecture 5 Principal Component Analysis (PCA)
    Description: Here we will discuss dimensionality reduction through SVD decomposition
    [Notes]

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  • Lecture 6 PCA II
    Description: Here we will continue our discussion on PCA
    [Notes]
  • Lecture 7 PCA III
    Description: We will conclude the discussion on PCA
    [Notes]
  • Lecture 8 Neural Networks
    Description: We will introduce the neural network model for deep learning
    [Notes]
  • Lecture 9 Autoencoders
    Description: We will introduce the concept of Autoencoders
    [Notes]
  • Lecture 10 Autoencoders II
    Description: We will discuss Autoencoders with linear and convolutional layers
    [Notes]
  • Lecture 12 Probabilistic PCA
    Description: We will start the discussion of generative models with Probabilistic PCA
    [Notes]
  • Lecture 13 Probabilistic PCA II
    Description: We will continue with Probabilistic PCA
    [Notes]

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  • Lecture 14 Probabilistic PCA III
    Description: We will conclude Probabilistic PCA with code implementation in python
    [Notes]
  • Lecture 15 Variational Autoencoders I
    Description: We will start with Variational Autoencoders
    [Notes]

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  • Lecture 16 Variational Autoencoders II
    Description: We will continue discussing Variational Autoencoders
    [Notes]
  • Lecture 19 Variational Autoencoders III
    Description: We will continue discussing Variational Autoencoders
    [Notes]
  • Lecture 20 Variational Autoencoders IV
    Description: We will continue discussing Variational Autoencoders
    [Notes]
  • Lecture 21 Variational Autoencoders V
    Description: We will be discussing Variational Autoencoders with full covariance matrix for the encoder
    [Notes]
  • Lecture 23 VAE conclusion
    Description: We will be concluding VAEs and we will also discuss working with custom datasets

    Suggested Readings:

  • Lecture 24 Gradient Descent for Deep Learning I
    Description: We will be discussing the basics of deriving gradients for optimizing neural networks
    [Notes]
  • Lecture 25 Gradient Descent for Deep Learning II
    Description: We will be concluding the basic discussion on optimizing neural networks
    [Notes]
  • Lecture 26 Gradient Descent for Deep Learning III
    Description: We will implement a regression and classification neural network from scratch in python