Unit Name

Machine Learning For Industrial Automation

Unit Code

ME605

 

Unit Duration

12 weeks

Award

Graduate Diploma of Engineering (Industrial Automation)

Duration: 1 year

 

Master of Engineering (Industrial Automation)

Duration: 2 years

Year Level

2nd

Unit Creator/Reviewer

Hadi Harb

Core/Elective

Core

Pre/Co-requisites

None

Credit Points

3

 

Grad Dip total course credit points = 24

(3 credits x 8 (units))

 

Masters total course credit points = 48

(3 credits x 12 (units) + 12 credits (Thesis))

Mode of Delivery

On-Campus or Online

Unit Workload

10 hours per week:

Lecture - 1 hour

Tutorial Lecture - 1 hours

Practical / Lab - 1 hour (where applicable)

Personal Study recommended - 7 hours (guided and unguided)

             

Unit Description and General Aims

This unit addresses machine learning and its application to industrial automation.

In this unit the student will be introduced to supervised learning, clustering, regression and time-series analysis. Data pre-processing and system evaluation will be explored.

A series of sub-topics will address the characteristics of commonly used algorithms such as Decision Trees, K-Nearest Neighbours, Neural Networks, linear regression, and K-Means Clustering.

 Different applications of machine learning to industrial automation will be explored. This will include condition monitoring, system identification, and image processing for autonomous vehicles. Software tools that can be used to implement machine learning algorithms will be presented. The student will be able to use such tools to apply machine learning to a particular industrial automation problem.

Learning Outcomes

On successful completion of this subject/unit, students are expected to be able to:

  1. Judge the applicability of machine learning to an industrial automation problem.

Bloom’s Level 5.

  1. Plan and execute data pre-processing to a machine learning problem.

Bloom’s Level 6

  1. Evaluate a machine learning algorithm.

Bloom’s Level 5.

  1. Design and implement a machine learning system to solve an industrial automation problem.

Bloom’s Level 6.

 

Bloom’s Taxonomy

The cognitive domain levels of Bloom’s Taxonomy:

Bloom’s Level

Bloom’s Category

Description

Verbs

1

Remember

Exhibit memory of previously learned material by recalling facts, terms, basic concepts, and answers.

Choose, Define, Find, How, Label, List, Match, Name, Omit, Recall, Relate, Select, Show, Spell, Tell, What, When, Where, Which, Who, Why

2

Understand

Demonstrate understanding of facts and ideas by organizing, comparing, translating, interpreting, giving descriptions, and stating main ideas.

Classify, Compare, Contrast, Demonstrate, Explain, Extend, Illustrate, Infer, Interpret, Outline, Relate, Rephrase, Show, Summarize, Translate

3

Apply

Solve problems to new situations by applying acquired knowledge, facts, techniques and rules in a different way.

Apply, Build, Choose, Construct, Develop, Experiment with, Identify, Interview, Make use of, Model, Organize, Plan, Select, Solve, Utilize

4

Analyse

Examine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations.

Analyse, Assume, Categorize, Classify, Compare, Conclusion, Contrast, Discover, Dissect, Distinguish, Divide, Examine, Function, Inference, Inspect, List, Motive, Relationships, Simplify, Survey, Take part in, Test for, Theme

5

Evaluate

Present and defend opinions by making judgments about information, validity of ideas, or quality of work based on a set of criteria.

Agree, Appraise, Assess, Award, Choose, Compare, Conclude, Criteria, Criticize, Decide, Deduct, Defend, Determine, Disprove, Estimate, Evaluate, Explain, Importance, Influence, Interpret, Judge, Justify, Mark, Measure, Opinion, Perceive, Prioritize, Prove, Rate, Recommend, Rule on, Select, Support, Value

6

Create

Compile information together in a different way by combining elements in a new pattern or proposing alternative solutions.

Adapt, Build, Change, Choose, Combine, Compile, Compose, Construct, Create, Delete, Design, Develop, Discuss, Elaborate, Estimate, Formulate, Happen, Imagine, Improve, Invent, Make up, Maximize, Minimize, Modify, Original, Originate, Plan, Predict, Propose, Solution, Solve, Suppose, Test Theory

 

Engineers Australia

The Australian Engineering Stage 1 Competency Standards for the Professional Engineer, approved as of 2013. This table is referenced in the mapping of graduate attributes to learning outcomes and via the learning outcomes to student assessment.

 

Stage 1 Competencies and Elements of Competency

1.

Knowledge and Skill Base

1.1

Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.

1.2

Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.

1.3

In-depth understanding of specialist bodies of knowledge within the engineering discipline.

1.4

Discernment of knowledge development and research directions within the engineering discipline.

1.5

Knowledge of engineering design practice and contextual factors impacting the engineering discipline.

1.6

Understanding of the scope, principles, norms, accountabilities and bounds of sustainable engineering practice in the specific discipline.

2.

Engineering Application Ability

2.1

Application of established engineering methods to complex engineering problem solving.

2.2

Fluent application of engineering techniques, tools and resources.

2.3

Application of systematic engineering synthesis and design processes.

2.4

Application of systematic approaches to the conduct and management of engineering projects.

3.

Professional and Personal Attributes

3.1

Ethical conduct and professional accountability.

3.2

Effective oral and written communication in professional and lay domains.

3.3

Creative, innovative and pro-active demeanour.

3.4

Professional use and management of information.

3.5

Orderly management of self, and professional conduct.

3.6

Effective team membership and team leadership.

Graduate Attributes

Successfully completing this Unit will contribute to the recognition of attainment of the following graduate attributes aligned to the AQF Level 9 criteria, Engineers Australia Stage 1 Competency Standards for the Professional Engineer and the Washington Accord and the Program Level Outcomes (PLO):

 

Graduate Attributes / Program Level Outcomes

(Knowledge, Skills, Abilities, Professional and Personal Development)

EA Stage 1 Competencies

Learning Outcomes

A. Effective Communication (PLO 1)

A1. Cognitive and technical skills to investigate, analyse and organise information and ideas and to communicate those ideas clearly and fluently, in both written and spoken forms appropriate to the audience.

2.2, 3.2

1

A2. Ability to professionally manage oneself, teams, information and projects and engage effectively and appropriately across a diverse range of international cultures in leadership, team and individual roles.

2.4, 3.2, 3.4, 3.5, 3.6

4

B. Critical  Judgement (PLO 2)

 

B1. Ability to critically analyse and evaluate complex information and theoretical concepts.

1.1, 1.2, 1.3, 2.1

3

B2. Ability to creatively, proactively and innovatively apply theoretical concepts, knowledge and approaches with a high level of accountability, in an engineering context.

1.5, 2.1, 3.3, 3.4

2, 3, 4

C. Design and Problem Solving Skills (PLO 3)

 

C1. Cognitive skills to synthesise, evaluate and use information from a broad range of sources to effectively identify, formulate and solve engineering problems.

1.5, 2.1, 2.3

1, 2, 3, 4

C2. Technical and communication skills to design complex systems and solutions in line with developments in engineering professional practice.

2.2, 2.3

2, 4

C3. Comprehension of the role of technology in society and identified issues in applying engineering technology ethics and impacts; economic; social; environmental and sustainability.

1.5, 1.6, 3.1

1

D. Science and Engineering Fundamentals (PLO 4)

D1. Breadth and depth of mathematics, science, computer technology and specialist engineering knowledge and understanding of future developments.

1.1, 1.2, 1.3, 1.4

1, 2, 3, 4

D2. Knowledge of ethical standards in relation to professional engineering practice and research.

1.6, 3.1, 3.5

1, 3

D3. Knowledge of international perspectives in engineering and ability to apply various national and International Standards.

1.5, 1.6, 2.4, 3.4

1, 3

E. Information and Research Skills (PLO 5)

E1. Application of advanced research and planning skills to engineering projects.

1.4, 2.4, 3.6

2, 3, 4

E2. Knowledge of research principles and methods in an engineering context.

1.4, 1.6

2, 3, 4

       

 

Unit Content and Learning Outcomes to Program Level Outcomes (PLO) via Bloom’s Taxonomy Level

This table details the mapping of the unit content and unit learning outcomes to the PLOs and graduate attributes at the corresponding Bloom’s Taxonomy level, specified by the number in the table.

 

Integrated Specification /

Program Learning Outcomes

PLO 1

PLO 2

PLO 3

PLO 4

PLO 5

Unit Learning Outcomes

LO1

5

-

5

5

-

LO2

-

6

6

6

6

LO3

-

5

5

5

5

LO4

6

6

6

6

6

Unit Study

Assessments

6

6

6

6

6

Lectures/Tutorials

6

6

6

6

6

 

 

 

 

 

 

 

Max Bloom’s level

6

6

6

6

6

Total PLO coverage

3

5

6

6

5

 

Student Assessment 

 

Assessment Type

(e.g. Assignment - 2000 word essay (specify topic)

Examination (specify length and format))

When assessed

(e.g. After Topic  5)

Weighting

(% of total unit marks)

Learning Outcomes Assessed

Assignment 1

Type: Multi-choice test / Group work / Short answer questions / Role Play / Self-Assessment / Presentation

Example Topic: To be suggested by the lecturer

After Topic 6

15%

1, 2

Assignment 2 – Project Midterm

Type: Report / Research / Paper / Case Study / Site Visit / Problem analysis / Project / Professional recommendation

(Typical report 1,500 words maximum, excluding references. This is a progress report with: problem analysis, literature review, hypothesis, and proposal for workings)

Example Topic: Proposal for the analysis, design and implementation of a machine learning solution applied to condition monitoring, or system identification, or autonomous vehicle vision.

After Topic 8

25%

1, 2, 4

Assignment 3 

Type: Presentation, discussion, group work, exercises, self-assessment/reflection, case study analysis, application.

Example Topic: Read a published paper in the domain of machine learning as applied to industrial automation. Prepare and present a 5-10 slides presentation of the paper summarizing: the problem, the data pre-processing applied, the proposed solution, the obtained results, and the social impact.

After Topic 10

15%            

1, 2

Assignment 4 – Final Project

Type: Report / Research / Paper / Case Study / Site Visit / Problem analysis / Project / Professional recommendation

(Typical thesis 4000 words, excluding references, figures and tables. This should complete the midterm report by adding sections on: workings, implementation, results, verification/validation, conclusion/challenges and recommendations/future work.)

Embedded practical component: Students are to design and simulate a machine learning system using Matlab or similar software tools and include results in final project report.

Final Week

40%

1, 2, 3, 4

Attendance / Tutorial Participation            

Example: Presentation, discussion, group work, exercises, self-assessment/reflection, case study analysis, application.

Continuous

5%            

-

 

 

Prescribed and recommended readings

Required textbook

  • [Available on Knovel] Bonnin, Rodolfo. (2017). Machine Learning for Developers. ISBN 978-1-78646-987-8, Packt Publishing.

Recommended textbooks

  • [Available on Knovel] Lucci, Stephen Kopec, Danny. (2016). Artificial Intelligence in the 21st Century. 2nd Edition. ISBN 978-1-942270-00-3, Mercury Learning and Information.
  • [Available on Knovel] Davies, E. R. (2018). Computer Vision - Principles, Algorithms, Applications, Learning. 5th Edition. ISBN 978-1-942270-00-3, Elsevier.

 

Reference Materials

Number of peer-reviewed journals and websites (advised during lectures). Some examples are listed below.

  1. Engineering Applications of Artificial Intelligence, Elsevier
  2. Machine Learning and Knowledge Extraction, MDPI
  3. IEEE Transactions on Evolutionary Computation, IEEE
  4. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE
  5. Internet of Things: Engineering Cyber Physical Human Systems, Elsevier
  6. Other material advised during the lectures.

 

Unit Content:

One topic is delivered per contact week, with the exception of part-time 24-week units, where one topic is delivered every two weeks.

 

Topic 1

Introduction to Machine Learning

  1. Classification
  2. Regression
  3. Time-series analysis
  4. Supervised learning
  5. Clustering
  6. Knowledge representation

 

Topic 2

Data pre-processing and system evaluation

  1. Feature vectors
  2. Feature selection
  3. Dimensionality reduction (Principle Component Analysis)
  4. Data preparation into training, validation and test datasets
  5. Cross-validation
  6. ROC (Receiver Operating Characteristic) curve
  7. Recall-precision curves
  8. Evaluating numeric prediction: root mean-squared error, root relative squared error, correlation coefficient
  9. Overfitting and generalization

 

Topic 3

Python for Machine Learning

  1. Review of Pandas, Numpy and Matplotlib
  2. Scikit-learn
  3. Statsmodels
  4. Tensorflow

 

 

Topic 4

Machine Learning Techniques - 1

  1. K-Nearest Neighbours
  2. Naïve Bayes
  3. Decision Trees
  4. Association rules
  5. K-Means clustering

 

Topic 5

Machine Learning Techniques - 2

  1. Numeric prediction: Linear Regression
  2. Decision boundaries
  3. Linear classification: Logistic Regression
  4. Linear classification: The Perceptron

 

 

Topic 6

Machine Learning Techniques - 3

  1. Neural Networks
  2. Multilayer Perceptron
  3. Gradient descent
  4. Training using error backpropagation
  5. Neural Networks as classifiers
  6. Neural Networks to learn functions

 

 

Topic 7

Machine Learning Techniques - 4

  1. Deep Learning
  2. Deep Feedforward Networks
  3. Convolutional Neural Networks
  4. Recurrent Neural Networks
  5. Deep Learning applications

 

 

Topic 8

Machine Learning Software Tools

  1. WEKA
    • WEKA Explorer
    • Data pre-processing
    • Building and testing classifiers
    • Clustering
    • Creating Association rules
  2. Matlab/Octave
  3. R
  4. Cloud-based platforms
    • Amazon
    • Microsoft
    • Google
    • IBM

Topic 9

Machine Learning Applications in Industrial Automation - 1

  1. Industrial IoT
  2. Artificial Intelligence for Industrial IoT
    • Smart instruments as data generators
  3. Product demand forecasting

For each cited application, the problem will be analysed, the machine learning solution presented, and the obtained results will be discussed.

 

 

Topic 10

Machine Learning Applications in Industrial Automation - 2

  1. Predictive maintenance
  2. Condition monitoring
  3. System identification

For each cited application, the problem will be analysed, the machine learning solution presented, and the obtained results will be discussed.

 

 

Topic 11

Machine Learning Applications in Industrial Automation - 3

  1. Autonomous vehicles
    • Localization
    • Movement planning
    • Scene understanding
  2. Image processing for autonomous vehicles
    • Classification
    • Clustering

 

For each cited application, the problem will be analysed, the machine learning solution presented, and the obtained results will be discussed.

 

 

Topic 12

Review

In the final weeks students will have an opportunity to review the contents covered so far. Opportunity will be provided for a review of student work and to clarify any outstanding issues.  Instructors/facilitators may choose to cover a specialized topic if applicable to that cohort.

Software/Hardware Used

Software

Hardware

  •  N/A