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42028: Assignment 1 – Autumn 2019 Page 1 of 4
Faculty of Engineering and Information Technology
School of Software
42028: Deep Learning and Convolutional Neural Networks
Autumn 2019
ASSIGNMENT-1 SPECIFICATION
Due date Friday 11:59pm, 19 April 2019 (Extended!)
Demonstrations Optional, If required.
Marks 30% of the total marks for this subject
Submission 1. A report in PDF or MS Word document (5-pages max)

2. Google Colab/iPython notebooks
   Submit to UTS Online assignment submission
   Note: This assignment is individual work.
   Summary
   This assessment requires you to develop three different classifiers namely, KNN,
   SVM and Neural network, for handwritten digit classification. The features to used
   for classification can be either Histogram-Of-Oriented-Gradients (HoG) or Local
   Binary Pattern(LBP), and raw images/pixels.
   Students need to provide the code (ipython Notebook) and a final report for the
   assignment, which will outline a brief comparative study of the classifier’s
   performance.
   Assignment Objectives
   The purpose of this assignment is to demonstrate competence in the following
   skills.
   To ensure firm understanding of basic machine learning basics. This will facilitate
   understanding of advanced topics.
   To ensure that students understand the basics of image classification, feature
   extraction using the traditional machine learning techniques.
   42028: Assignment 1 – Autumn 2019 Page 2 of 4
   Tasks:
   Description:
3. Implement a simple kNN classifier for digit classification
4. Implement a Linear classifier using SVM for digit classification
5. Implement a Linear classifier using Neural Network for digit classification
6. Compare the three implementations in terms of classification accuracy.
   Write a short report on the implementation, linking the concepts and methods
   learned in class, and also provide comparative study on the accuracies obtained
   from combination of different classifiers and features.
   Features to used: Any least two from the list given below:
   a. HoG
   b. LBP
   c. Raw image/pixels values
   d. Any other feature of your choice
   Dataset to be used: MNIST (English handwritten numerals).
   Report Structure:
   The report should include the following sections:
7. Introduction: Provide a brief outline of the report and also briefly explain
   the features and classifier combination used for experiments.
8. Dataset: Provide a brief description of the dataset used with some sample
   images of each class.
9. Experimental results and discussion:
   a. Experimental settings: Provide information on the classifier settings
   (e.g: KNN: value of k for kNN classifier; SVM: kernel and other
   parameters used in SVM classifier; ANN: number of input
   neurons/nodes, activation function, loss function, output layer
   information etc.)
   b. Experimental Results:
   i. Confusion matrix for the highest accuracy achieved, with a
   very short description, with some result image sample
   (optional)
   ii. Comparative study: sample table format
   Classifier/Feature HOG LBP Raw Input
   iii. Discussion: Provide your understanding on why there was an
   error in the accuracy, and difference in the performance of the
   classifiers. You may also include some image samples which
   were wrongly classified.
   42028: Assignment 1 – Autumn 2019 Page 3 of 4
10. Conclusion: Provide a short paragraph detailing your understanding on the
    experiments and results.
    Deliverables:
11. Project Report (5 pages max)

12. Google Colab or Ipython notebook, with the code
    Additional Information:
    Assessment Submission
    Submission of your assignment is in two parts. You must upload a zip file of the
    Ipython/Colab notebooks and Report to UTS Online. This must be done by the Due
    Date. You may submit as many times as you like until the due date. The final
    submission you make is the one that will be marked. If you have not uploaded your zip
    file within 7 days of the Due Date, or it cannot be run in the lab, then your assignment
    will receive a zeromark. Additionally, the result achieved and shown in the
    ipython/colab notebooks should match the report. Penalties apply if there are
    inconsistencies in the experimental results and the report.
    PLEASE NOTE 1: It is your responsibility to make sure you have thoroughly tested your
    program to make sure it is working correctly.
    PLEASE NOTE 2: Your final submission to UTS Online is the one that is marked. It does
    not matter if earlier submissions were working; they will be ignored. Download your
    submission from UTS Online and test it thoroughly in your assigned laboratory.
    Return of Assessed Assignment
    It is expected that marks will be made available 2 weeks after the submission via UTS
    Online. You will be given a copy of the marking sheet showing a breakdown of the marks.
    Queries
    If you have a problem such as illness which will affect your assignment submission
    contact the subject coordinator as soon as possible.
    Dr. Nabin Sharma
    Room: CB11.07.124
    Phone: 9514 1835
    If you have a question about the assignment, please post it to the UTS Online forum
    for this subject so that everyone can see the response.
    If serious problems are discovered the class will be informed via an announcement on UTS
    Online. It is your responsibility to make sure you frequently check UTS Online.
    PLEASE NOTE : If the answer to your questions can be found directly in any of the
    42028: Assignment 1 – Autumn 2019 Page 4 of 4
    following
    subject outline
    assignmentspecification
    UTS Online FAQ
    UTS Online discussion board
    You will be directed to these locations rather than given a direct answer.
    Extensions and Special Consideration
    In alignment with Faculty policies, assignments that are submitted after the Due
    Date will lose 10% of the received grade for each day, or part thereof, that the
    assignment is late. Assignments will not be accepted after 5 days after the Due Date.
    When, due to extenuating circumstances, you are unable to submit or present an
    assessment task on time, please contact your subject coordinator before the
    assessment task is due to discuss an extension. Extensions may be granted up to a
    maximum of 5 days (120 hours). In all cases you should have extensions confirmed in
    writing.
    If you believe your performance in an assessment item or exam has been adversely
    affected by circumstances beyond your control, such as a serious illness, loss or
    bereavement, hardship, trauma, or exceptional employment demands, you may be
    eligible to apply for Special Consideration (https://www.uts.edu.au/curren…
    .
    Academic Standards and Late Penalties
    Please refer to subject outline.

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