cse2mlx machine learning
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
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Machine Learning: Hands-On for Developers and Technical Professionals | Prescribed | Bell J., 2015 | Wiley |
| Readings | Amazon Web Services online training | Recommended | Amazon Web Services Educate | Amazon 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…
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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 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 ClassWeek: 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 element | Comments | % | ILO* |
|---|---|---|---|
| Online test (250-words equiv.) | Multiple-choice and/or short answer questions test on fundamentals of machine learning in cloud environment Timed assessment | 10 | 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 problem | 30 | 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 XD | 35 | 04, 05, 06 |
| Online subject test (1000-words equiv.) | Multiple-choice and/or short answer questions test that covers all the topics Timed assessment | 25 | 04, 05 |