Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: EEE408
Module Title: Deep Learning in Computer Vision
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: Electrical and Electronic Engineering
Pre-requisites: N/A
Visual recognition tasks such as image classification, localization and detection are core to many of computer vision applications. Recent developments in neural network approaches (“deep learning”) have greatly advanced the performance of these state-of-the-art visual recognition systems. This course focuses on the details of these deep learning architectures, particularly image classification. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision and multimedia applications.
Learning outcomes 
A. Demonstrate expert knowledge to offer critical insight into the current state-of-the-art deep learning technologies.

B. Demonstrate deep understanding in relation to machine learning.

C. Demonstrate module specific practical skills, including creation of neural networks, ability to analysis deep learning algorithm performance.

D. Show the intellectual ability to provide critical analysis on the real-world computer vision problems and design suitable solutions based on available technologies.

E. Demonstrate ability to undertake individual research on current state-of-the-art deep learning technologies.

Method of teaching and learning 
This module will be delivered through a combination of formal lectures and lab sessions. The lab experiments will be performed using C/C++, MATLAB or PYTHON or other software tools for deep learning. For each lab a report shall be prepared, two of these will count towards the summative assessment for the module. A final examination consisting of a number of problem/design based questions as well as questions aimed at examining the students grasp of the subject theory will form the remainder of the summative assessment.
1. Introduction to computer vision and deep learning, historical context.

2. Image classification and the data-driven approach

k-nearest neighbor

Linear classification

Higher-level representations, image features

Optimization, stochastic gradient descent

3. Back-propagation

Introduction to neural networks

4. Training Neural Networks Part 1

activation functions, weight initialization, gradient flow, batch normalization

babysitting the learning process, hyperparameter optimization

5. Training Neural Networks Part 2: parameter updates, ensembles, dropout

Convolutional Neural Networks: introduction

6. Convolutional Neural Networks: architectures, convolution / pooling layers

Case study of ImageNet challenge winning ConvNets

7. ConvNets for spatial localization

Object detection

8. ConvNets for image segmentation

9. Understanding and visualizing Convolutional Neural Networks

10. Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM)

RNN language models

Image captioning

11. Advanced topic of other deep learning models e.g., Deep Reinforcement Learning, Restricted Boltzmann Machines (RBM) based DNN.

12. Overview of Caffe

13. Summary
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26      24    100  150 


Sequence Method % of Final Mark
1 Lab Report 40.00
2 Take-Home Open Book Exam (3 Hours) 60.00

Module Catalogue generated from SITS CUT-OFF: 6/2/2020 12:01:23 AM