MACHINE LEARNING

CSE2MLX

2019

Credit points: 15

Subject outline

This subject introduces students to the fundamentals of machine learning (history, algorithm types, uses) and then covers an efficient implementation of machine learning algorithms on real-world data of moderate complexity using an open source ecosystem and MapReduce and R environments. Students explore the practical aspects of this subject using the AWS platform.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorRabei Alhadad

Available to Study Abroad StudentsNo

Subject year levelYear Level 2 - UG

Exchange StudentsNo

Subject particulars

Subject rules

Prerequisites Must be admitted into SBACTO and must have passed CSE1CFX and CSE1IOX

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsMachine Learning: Hands-On for Developers and Technical ProfessionalsPrescribedBell J., 2015Wiley
ReadingsAmazon Web Services online trainingRecommendedAmazon Web Services EducateAmazon Web Services

Graduate capabilities & intended learning outcomes

01. Appraise machine learning as it can be applied to real-world problems in the cloud environment

Activities:
Webinar presentation and open discussions about the fundamentals of machine learning. Self- checking open-ended questions to support the activities.
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

02. Analyse the strengths and weaknesses of a wide variety of machine learning algorithms

Activities:
Webinar with open discussion forums. Knowledge assessed via online quizzes and self-paced online problems.
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

03. Apply machine learning algorithms to solve real-world problems of moderate complexity

Activities:
Activity on applying machine learning algorithms to solve real-world problems
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

04. Compare the various tools that are available for data collection and processing

Activities:
Webinar with open discussion forums. Knowledge assessed via online quizzes and self-paced online problems.
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

05. Integrate machine learning libraries, and mathematical and statistical tools with modern technologies like Hadoop distributed file system and MapReduce programming model

Activities:
Practical activity using a simulated environment
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

06. Employ R programming language to perform efficient implementation of machine learning algorithms on real data

Activities:
Practical activity using a simulated environment
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing,Quantitative Literacy)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Critical Thinking,Creative Problem-solving,Inquiry/Research)

Subject options

Select to view your study options…

Start date between: and    Key dates

Online (Didasko), 2019, Study Block 1, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 02 - 13
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 02 to week 13 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 2, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 06 - 17
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 06 to week 17 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 3, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 10 - 21
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 10 to week 21 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 4, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 14 - 25
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 14 to week 25 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 5, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 19 - 30
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 19 to week 30 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 6, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 23 - 34
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 23 to week 34 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 7, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 27 - 38
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 27 to week 38 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 8, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 32 - 43
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 32 to week 43 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 9, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 36 - 47
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 36 to week 47 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 10, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 41 - 52
One 3.0 hours unscheduled online class per week on any day including weekend during the day from week 41 to week 52 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 11, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 45
One 3.0 hours unscheduled online class per week on any day including weekend during the day in week 45 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05

Online (Didasko), 2019, Study Block 12, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorRabei Alhadad

Class requirements

Unscheduled Online Class Week: 49
One 3.0 hours unscheduled online class per week on any day including weekend during the day in week 49 and delivered via online.

Assessments

Assessment elementComments% ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment10 01
Written assignment on machine learning algorithm (1350-words equiv.)An assignment focused on identifying and applying the most appropriate learning algorithm to solve a real-world problem30 02, 03
Written assignment on other machine learning features (1600-words equiv.)A written practical scenario-based lab report on other machine learning features such as batch processing, R and Spring XD35 04, 05, 06
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topics Timed assessment25 04, 05