MACHINE LEARNING

CSE2MLX

2020

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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Rabei Alhadad

Available to Study Abroad/Exchange Students: No

Subject year level: Year Level 2 - UG

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: CSE1IOX AND CSE1CFX
Students must be admitted in the following course: SBACTO

Co-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Machine Learning: Hands-On for Developers and Technical Professionals

Resource Type: Book

Resource Requirement: Prescribed

Author: Bell J.

Year: 2015

Edition/Volume: N/A

Publisher: Wiley

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Amazon Web Services online training

Resource Type: Book

Resource Requirement: Recommended

Author: Amazon Web Services Educate

Year: N/A

Edition/Volume: N/A

Publisher: Amazon Web Services

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

COMMUNICATION - Communicating and Influencing

INQUIRY AND ANALYSIS - Creativity and Innovation

INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving

INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry

PERSONAL AND PROFESSIONAL - Adaptability and Self-Management

Intended Learning Outcomes

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

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

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

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

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

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

Online (Didasko), 2020, Study block 1, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 2 - 13
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 2 to week 13 and delivered via online.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 10, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 41 - 52
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 11, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 45 - 0
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 45 to week 0 and delivered via online.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 12, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 49 - 0
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 49 to week 0 and delivered via online.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 2, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 6 - 17
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 6 to week 17 and delivered via online.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 3, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 10 - 21
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 4, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 14 - 25
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 5, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 19 - 30
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 6, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 23 - 34
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 7, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 27 - 38
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 8, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 32 - 43
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5

Online (Didasko), 2020, Study block 9, Online

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Rabei Alhadad

Class requirements

Unscheduled Online ClassWeek: 36 - 47
One 3.00 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 element	Comments	Category	Contribution	Hurdle	%	ILO*
Online test (250-words equiv.)Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environmentTimed assessment	N/A	N/A	N/A	No	10	SILO1
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 problem	N/A	N/A	N/A	No	30	SILO2, SILO3
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 XD	N/A	N/A	N/A	No	35	SILO4, SILO5, SILO6
Online subject test (1000-words equiv.)Multiple-choice and/or short answer questions test that covers all the topicsTimed assessment	N/A	N/A	N/A	No	25	SILO4, SILO5