Artificial Intelligence and Deep Learning (COMP 20037)

Task 1 [100 Marks]

Choose a real-world binary image classification problem of your interest.

a. Apply deep learning model to solve the chosen problem using Python programming
language. Provide the screenshot of the code along with respective output [30 marks]

b. Provide the git hub link of the code. [5 marks]

c. Write a report that discusses the problem-solving process that must include the
following

i. Abstract [5 marks]

Note: Briefly describe your problem, approach, and key results (100-150 words)

ii. Introduction [5 marks]

Note: Provide a general introduction to deep learning and its applications in solving
real-world problems (150-200 words)

iii. Literature review [10 marks]

Note: Discuss published works (minimum 2) that relate to your project. How is your

approach similar or different from others? (200-250 words)

iv. Problem statement [5 marks]

Note: Describe the problem you are working on, why is it important? (100-150
words)

v. Data [5 marks]

Note: Description of the dataset which includes a description of the data you are

working with for your case study. What type of data is it? What is the source of

data? How much data are you working with? Did you have to do any preprocessing

to use this data in your project? (100-150 words)

vi. Methods [10 marks]

Note: Discuss your approach for solving the problems that you set up in task (iv)

which includes the description of the proposed convolutional neural network model

and the pre-trained model used. You should demonstrate that you have applied

ideas and skills built up during the semester to tackle your problem of choice. (200-
250 words)

vii. Result Evaluation [20 marks]

Note: The following need to be considered. (250-300 words)

a. Impact of overfitting/underfitting and respective solution (at least one solution)

b. Include the performance evaluation of your method and compare it with the

pre-trained model based on at least 2 classification evaluation metrics. Show the

comparison in the form of a table.

viii. Conclusion [5 marks]

Note: Summarize your key results - what have you learned? Suggest ideas for future
extensions or new applications of your ideas. (100-150 words)

Note:

The approval of the problem identified should be sought from the module instructor by
week 10.

Penalty of 5 or more marks will be applied if

o no comments are provided in the code

o a proper flow is not followed in the code and report

o no output is screenshot along with the code

o violated the word limit in the report

Rules & Regulations:

Softcopy in Word format is to be submitted through the Turnitin link on MEC Learn.

Viva may be conducted depending on the need after the submission.

Guidelines:

Use Jupyter notebook / Google colab / any online editor to use Python

Use Microsoft Word to type the report (Times New Roman font, 1.5 line spacing, 12 font size for
the body, 14 font size for the heading)

A single Word file should be uploaded on MEC Learn

Git hub link should be provided in the Word file

For the student academic integrity policy, please visit MEC Learn ? Academic Policies and General
Information

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