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Convolutional Neural Network

Course Introduction

  • Course Name: Convolutional Neural Network
  • Course Code: CSH4R3
  • Credits : 3
    • Deep Learning on Neural Network
    • Specific example on Computer Vision

Prerequisite: Basic Knowledge Required

  • Proficiency in Algorithm and Programming
  • Calculus, Linear Algebra
  • Artificial Intelligence

Prerequisite: Equivalent knowledge

  • Digital Image Processing
  • Machine Learning
  • Visual Recognition System

Course Objective

  • Specific materials aimed to support your thesis
    • Specific example on Computer Vision
  • Understand and able to Implement Convolutional Neural Network
    • Write from scratch, debug and train CNN
  • Presenting the most recent research and developments around Neural Net and Deep Learning
    • Current State of the Art Research

Points

  • CLO 1 (40%) : ConvNet
    • Identify and explain recent advancement and developments of Convolutional Neural Network
  • CLO 2 (30%) : ConvNet API
    • Implement Convolutional Neural Network API from scratch (forward and backward API)
  • CLO 3 (30%) : ConvNet in Practice
    • Apply ConvNet to a specific case

Point Distribution

A 80 … 100.0
AB 75 … 79.9
B 70 … 74.9
BC 60 … 69.9
C 50 … 59.9
D 40 … 49.9
E 0 … 39.9

Lecture Slides

  • [tba]

Exercises

Class Exercises

  • [tba]

Previous Class

 


Teaching History


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