Schedule

This is a tentative schedule. It may change as the semester goes on.

  • Event
    Date
    Description
    Course Material
  • Lecture
    01/20/2026
    Tuesday
    Lec1 - Intro to Computer Vision
  • Lecture
    01/22/2026
    Thursday
    Lec2 - Linear Classifiers and Perceptron
  • Assignment
    01/22/2026
    Thursday
    Assignment #1 - The Perceptron Algorithm released!
  • Lecture
    01/27/2026
    Tuesday
    Lec3 - Multilayer Perceptron and Intro to Deep Learning
  • Exam
    01/29/2026
    Thursday
    Quiz 1

    Topics: Material covered from Lec2 and Lec3.

  • Lecture
    01/29/2026
    Thursday
    Lec4 - Optimization and Regularization
  • Assignment
    01/29/2026
    Thursday
    Assignment #2 - Multilayer Perceptron and Optimization released!
  • Lecture
    02/03/2026
    Tuesday
    Lec5 - Pytorch I – MLPs
  • Exam
    02/05/2026
    Thursday
    Quiz 2

    Topics: Material covered from Lec4 and Lec5.

  • Lecture
    02/05/2026
    Thursday
    Lec6 - Pytorch II – Images and Regularization
  • Lecture
    02/10/2026
    Tuesday
    Lec7 - Convolutional Neural Networks
  • Exam
    02/12/2026
    Thursday
    Quiz 3

    Topics: Material covered from Lec6 and Lec7.

  • Lecture
    02/12/2026
    Thursday
    Lec8 - Data Augmentation and Deep CNNs
  • Lecture
    02/17/2026
    Tuesday
    Lec9 - Transfer Learning and Residual Nets
  • Exam
    02/19/2026
    Thursday
    Quiz 4

    Topics: Material covered from Lec8 and Lec9.

  • Lecture
    02/19/2026
    Thursday
    Lec10 - Inception Net and what CNNs learn
  • Lecture
    02/24/2026
    Tuesday
    Lec11 - Adversarial Examples and Self-supervision
  • Exam
    02/26/2026
    Thursday
    Quiz 5

    Topics: Material covered from Lec10 and Lec11.

  • Lecture
    02/26/2026
    Thursday
    Lec12 - Intro to MLOps
  • Lecture
    03/03/2026
    Tuesday
    Lec13 - Intro to Object Detection
  • Exam
    03/05/2026
    Thursday
    Quiz 6

    Topics: Material covered from Lec12 and Lec13.

  • Lecture
    03/05/2026
    Thursday
    Lec14 - Fast Object Detection
  • 03/10/2026
    Tuesday
    SPRING BREAK!

    No lecture.

  • 03/12/2026
    Thursday
    SPRING BREAK!

    No lecture.

  • 03/17/2026
    Tuesday
    SPRING BREAK!

    No lecture.

  • 03/19/2026
    Thursday
    SPRING BREAK!

    No lecture.

  • Lecture
    03/24/2026
    Tuesday
    Lec15 - Intro to Image Segmentation
  • Exam
    03/26/2026
    Thursday
    Quiz 7

    Topics: Material covered from Lec14 and Lec15.

  • Lecture
    03/26/2026
    Thursday
    Lec16 - Applications of Detection and Segmentation
  • Lecture
    03/31/2026
    Tuesday
    Lec17 - Autoencoders
  • Exam
    04/02/2026
    Thursday
    Quiz 8

    Topics: Material covered from Lec16 and Lec17.

  • Lecture
    04/02/2026
    Thursday
    Lec18 - Image Generation with GANs
  • Lecture
    04/07/2026
    Tuesday
    Lec19 - Advanced GANs
  • Exam
    04/09/2026
    Thursday
    Quiz 9

    Topics: Material covered from Lec18 and Lec19.

  • Lecture
    04/09/2026
    Thursday
    Lec20 - The Attention Mechanism
  • Lecture
    04/14/2026
    Tuesday
    Lec21 - Transformers and ChatGPT
  • Lecture
    04/16/2026
    Thursday
    Lec22 - Image Generation by Prompt
  • Exam
    04/21/2026
    Tuesday
    Final Quiz

    All the material covered during the whole course may show up. It will be 3-4x longer than the usual quiz.

  • Guest Lecture
    04/23/2026
    Thursday
    Guest No. 1

    Mandatory student presence.

  • Project
    04/28/2026
    Tuesday
    Work on final projects.

    No lecture. Students will work on final projects. Mandatory student presence. Instructor will be there to help students out with their projects!

  • Project
    04/30/2026
    Thursday
    Work on final projects.

    No lecture. Students will work on final projects. Mandatory student presence. Instructor will be there to help students out with their projects!

  • Project
    05/05/2026
    Tuesday
    Work on final projects.

    No lecture. Students will work on final projects. Mandatory student presence. Instructor will be there to help students out with their projects!