ENG335 Machine Learning Submission You are to submit the ECA assignment in exactly the same manner as your tutor-marked assignments (TMA), i

Question

ENG335 Machine Learning
Submission

You are to submit the ECA assignment in exactly the same manner as your tutor-marked assignments (TMA), i.e. using Canvas. Submission in any other manner like hardcopy or any other means will not be accepted. You are to ensure that the file to be submitted does

not exceed 20MB in file size.

Electronic transmission is not immediate. It is possible that the network traffic may be particularly heavy on the cut-off date and connections to the system cannot be guaranteed.

Hence, you are advised to submit your assignment the day before the cut-off date in order to make sure that the submission is accepted and in good time.

Once you have submitted your ECA assignment, the status is displayed on the computer screen. You will only receive a successful assignment submission message if you had applied for the e-mail notification option.

ECA Marks Deduction Scheme

Please note the following:

a) Submission Cut-off Time – Unless otherwise advised, the cut-off time for ECA submission will be at 12:00 noon on the day of the deadline. All submission timings

will be based on the time recorded by Canvas.

b) Start Time for Deduction – Students are given a grace period of 12hours. Hence

calculation of late submissions of ECAs will begin at 00:00 hrs the following day

(this applies even if it is a holiday or weekend) after the deadline.

c) How the Scheme Works – From 00:00 hrs the following day after the deadline, 10

marks will be deducted for each 24-hour block. Submissions that are subject to more

than 50 marks deduction will be assigned zero mark. For examples on how the

scheme works, please refer to Section 5.2 Para 1.7.3 of the Student Handbook.

Any extra files, missing appendices or corrections received after the cut-off date will also

not be considered in the grading of your ECA assignment.

Plagiarism and Collusion

Plagiarism and collusion are forms of cheating and are not acceptable in any form of a

student’s work, including this ECA assignment. You can avoid plagiarism by giving

appropriate references when you use some other people’s ideas, words or pictures (including

diagrams). Refer to the American Psychological Association (APA) Manual if you need

reminding about quoting and referencing. You can avoid collusion by ensuring that your

submission is based on your own individual effort.

The electronic submission of your ECA assignment will be screened through a plagiarism

detecting software. For more information about plagiarism and cheating, you should refer

to the Student Handbook. SUSS takes a tough stance against plagiarism and collusion.

Serious cases will normally result in the student being referred to SUSS’s Student

ECA – July Semester 2022 Page 3 of 4

Disciplinary Group. For other cases, significant marking penalties or expulsion from the

course will be imposed.

Additional Instructions for Submission:

1. Please submit a Word document with screenshots of the Jupyter notebook and a

zipped folder container Jupyter notebook file (.ipynb) for each question in the

respective folders via the Canvas T- group.

2. All answers for each question should be indicated clearly using the Comments

section/markups in the Notebook so that the marker can see clearly which code is

for which Question. (e.g. # Answer for Q1a).

You are required to prepare the dataset wherever necessary.

_____________________________________________________________________

Question 1

Download the SGEMM GPU kernel performance dataset from the below link.

https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance

Understand the dataset by performing exploratory analysis. Prepare the target

parameter by taking the average of the THREE (3) runs with long performance times.

Design a linear regression model to estimate the target using only THREE (3) attributes

from the dataset. Discuss your results, relevant performance metrics and the impact of

normalizing the dataset.

(20 marks)

Question 2

Load the wine dataset from sklearn package. Perform exploratory data analysis and

Design a simple TWO (2) layer neural network for the classification. Compare the

performance with the Naïve Bayes algorithm. Train the neural network such that it has

better or same performance as that of the Naïve Bayes algorithm.

(20 marks)

Question 3

Download the MAGIC gamma telescope data 2004 dataset available in Kaggle

(https://www.kaggle.com/abhinand05/magic-gamma-telescope-dataset). Prepare the

dataset and perform exploratory data analysis. Set-up a random forest algorithm for

identifying whether the pattern was caused by gamma signal or not. Propose optimal

values for the depth and number of trees in the random forest. Assess and compare the

performance of optimized random forest with the Naïve Bayes algorithm. Discuss the

performance metrics and the computational complexity.

(20 marks)

ECA – July Semester 2022 Page 4 of 4

Question 4

Use the Fashion MNIST dataset from the keras package. Perform exploratory data

analysis. Show a random set of FIVE (5) images from each class in the dataset with

their corresponding class names. Prepare the dataset by normalizing the pixel values to

be between 0 and 1. Design a CNN with TWO (2) convolutional layers and FOUR (4)

dense layers (including the final output layer). Employ ‘ReLU’ activation and

‘MaxPooling’. Keep 15% of the train dataset for validation. Rate the performance of

the algorithm and provide necessary plots. Pick a random image from the test dataset,

pass it to the algorithm and compare the algorithm output with the actual class label.

(20 marks)

Question 5

Select any stock listed in Singapore stock exchange. Using Yahoo finance, download

the daily stock data (Open, High, Low, Close, Adj Close, Volume) from year 1 Jan

2020 to 3 Jan 2022. Use data until 31 Dec 2020 for training and the remaining data for

testing. You must select the stock such that the data is available from 1 Jan 2020 to 3

Jan 2022. Use previous 30 days of stock information to predict the next day stock price.

Use the data in ‘High’ column to predict the price, i.e., the next day high price of the

stock. Design a LSTM network to do the predictions. You are required to use LSTM

with a cell state of at least 60 dimension and do at least 50 epochs of training. Rate the

performance of the LSTM classifier and provide necessary plots.
1. Please submit a Word document with screenshots of the Jupyter notebook and a zipped folder container Jupyter notebook file (.ipynb) for each question. 2. All answers for each question should be indicated clearly using the Comments section/markups in the Notebook so that the marker can see clearly which code is for which Question. (e.g. # Answer for Q1a).