代写42028留学生、代做Neural Networks、Python程序代写、代做Python语言

日期：2019-04-21 08:23

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:

1. Implement a simple kNN classifier for digit classification

2. Implement a Linear classifier using SVM for digit classification

3. Implement a Linear classifier using Neural Network for digit classification

4. 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:

1. Introduction: Provide a brief outline of the report and also briefly explain

the features and classifier combination used for experiments.

2. Dataset: Provide a brief description of the dataset used with some sample

images of each class.

3. 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

4. Conclusion: Provide a short paragraph detailing your understanding on the

experiments and results.

Deliverables:

5. Project Report (5 pages max)

6. 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/currentstudents/managing-your-course/classes-and-assessment/specialcircumstances/special)

.

Academic Standards and Late Penalties

Please refer to subject outline.