CIS006-2 Concepts and Technologies of Artificial Intelligence Assignment 01 Brief | University of Bedfordshire

What am I required to do in this assignment?

Task 

Students will develop a Machine Learning (ML) solution to solve a biometric recognition task with the highest recognition accuracy. The facial images are taken from real subjects in slightly different conditions, and so some images can be incorrectly recognised. This makes the ideal 100% accurate recognition difficult or even impossible. Students will design a ML solution providing the minimal recognition error.

Examples

Students who studied this unit have achieved excellent results in Biometric Face Recognition published as follows: 

• journal paper 
• conference paper in Springer proceedings 
• Springer book chapter 
• conference paper

Examples of previous assignment reports will also be discussed. Alternatively, students can use other benchmark data available in the Kagglesubject area. For example, students could be interested in early detection of bone pathologies in X-ray images, as described in a paper published in Scientific Reports. 

Methods and Technologies 

To achieve the minimum error, students will use ML techniques such as Artificial Neural Networks (ANNs) which can be implemented by using a new powerful programming platform supporting languages related to ML. Alternatively, advanced students can use other programming platforms using programming languages such as Python, MATLAB, or R. Advanced students can also be interested in a high-performance ML techniques such as Deep Learning, Convolutional Networks, and/or Gradient Boosting, demanded on the job market. The Google Colab is a recommended platform, however advanced students can use Integrated Development Environments such as Spyder. 

Project Data and Scripts 

The project's biometric data include facial images of 30 persons. Each person is represented by 50 images taken under different conditions. When students use Colab, the data zip file has to be uploaded to your Google Drive root. The project scriptsprocess_yale_images andclassify_yalehave to be uploaded to your Colab project.

Individual Reports

Students will run individual experiments by using the project scripts on a benchmark data set. First students are expected to achieve the unit threshold requirements, and then they could develop work to a higher grade. The following template can be used for individual reports. To make submissions on BREO, students need to check the Similarity of reports which is not recommended higher than 20%. 

Is there a size limit?

2000 words on average 

Learning Outcomes

• Demonstrate results of using an established AI technique which is capable of finding a solution to a given AI problem represented by a data set
• Identify the cases of correct and incorrect outcomes generated by the technique on the given data set
• Evaluate the accuracy of the technique in terms of rates of correct outcomes