top of page

2021 Fall: Intelligent Image Recognition (지능형영상인식)

Lecture Notes

  • Due to COVID-19, we will continue online lectures with pre-recorded video lectures for some time.
    For each chapter, please download PPT(Lecture Video), and play it in slideshow mode.

  • Starting from Lecture 1, after watching each lecture video, each student needs to submit
    a lecture summary via Google Classroom. Check your email to find the Google Classroom
    link of CBNU/Intelligent Image Recognition. 


  • Lecture1: Introduction To Image Recognition [PPT (Lecture Video)]

  • Lecture2: Image Classification [PPT (Lecture Video)]

  • Lecture3: Convolutional Neural Network [PPT (Lecture Video)]

  • Lecture4: Training with Backpropagation [PPT (Lecture Video)]

  • Lecture5: CNN Architectures [PPT (Lecture Video)]

  • Lecture6: Segmentation and Detection [PPT (Lecture Video)]

  • Lecture7: Fast Detection CNNs [PPT (Lecture Video)]

  • Lecture8: Generative Models [PPT (Lecture Video)]

  • Lecture9: Generative Adversarial Networks [PPT (Lecture Video)]

  • Final Exam: December 6th (Monday) 7PM~9PM, Location: E8-7 Room 307,
    Open Note (Only Lecture Notes are allowed during the exam)

  • Term Project: Presentation on December 13th (Monday) 7PM~9PM, E10 Room 406

    • All students who are studying deep learning must select Option (1).
      Students whose major is not deep learning can select Option (2) with the professor's approval.

    • (1) Train a CNN model of your choice using a public dataset or your own dataset. Demonstrate the inference and accuracy (You can choose the same CNN as your research topic)

    • (2) Paper Survey on CNNs or GANs for classification, detection, segmentation, augmentation, and present the summary of your survey result. 

  • Overall Evaluation Results [Evaluation Scores]​

    • ​If you have any questions regarding your scores, please send me an inquiry email.

bottom of page