Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes. We acknowledge the public resources from Stanford CS231n Spring 2024, Lil’Log and other public materials as referenced and noted in the slides.

• 

  Trustworthy AI Systems Overview
  tl;dr: An overview of trustworthy AI system principles and course syllabus.
  [slides]
  

  Suggested Readings:

  • HHS Trustworthy AI Playbook
• 

  Deep Learning for Image Classification
  tl;dr: A review of deep learning basics and its application in image classifcation.
  [slides]
  

  Suggested Readings:

  • Deep Residual Learning for Image Recognition
  • A Survey of Convolutional Neural Networks
• 

  Deep Learning for Image Segmentation
  tl;dr: An introduction to semantic segmentation, object detection and instance segmentation.
  [slides]
  

  Suggested Readings:

  • Ultralytics
  • Image Segmentation Using Deep Learning
• 

  Generative Modeling (Part I)
  tl;dr: An introduction to generative adversarial network and neural style transfer.
  [slides]
  

  Suggested Readings:

  • Generative Adversarial Nets
  • Generative Adversarial Networks in Computer Vision
• 

  Generative Modeling (Part II)
  tl;dr: An introduction to variational autoencoder and diffusion models.
  [slides]
  

  Suggested Readings:

  • Diffusion Models in Vision
  • Diffusion Model Blog
  • Variational Autoencoder Blog
• 

  Deep Learning for Audio Recognition
  tl;dr: An introduction to speech and speaker recognition and human factors in deepfake audio detection.
  [slides]
  

  Suggested Readings:

  • X-Vector
  • Audio Deepfake Detection
• 

  Deep Learning for Voice Conversion
  tl;dr: An introduction to voice conversion techniques and potential attacks.
  [slides]
  

  Suggested Readings:

  • VC Paper/Project List
• 

  Pretrained Foundation Models
  tl;dr: An introduction to the recurrent neural network, attention, transformers and foundation models.
  [slides]
  

  Suggested Readings:

  • Foundation Model Paper 1
  • Foundation Model Paper 2
  • Building Large Language Models
• 

  Large Language Model Agent
  tl;dr: An introduction to the large language model agents.
  [slides]
  

  Suggested Readings:

  • LLM Powered Autonomous Agents
  • Application Development using LLMs
• 

  Hallucinations in Large Language Models
  tl;dr: An introduction to LLM hallucination causes, detection and mitigation.
  [slides]
  

  Suggested Readings:

  • Extrinsic Hallucinations in LLMs
  • A Survey on Hallucination in LLMs