Module Details

Module Code: PHYS8040
Title: Adv Prog for Inst & Analysis
Long Title: Adv Prog for Instrumentation & Analysis
NFQ Level: Advanced
Valid From: Semester 1 - 2024/25 ( September 2024 )
Duration: 1 Semester
Credits: 5
Field of Study: 4411 - Physics
Module Delivered in: no programmes
Module Description: This module builds on the material covered in the Introduction to Programming for Instrumentation and Analysis (PHYS6024) and Programming for Instrumentation and Analysis (PHYS7039) modules to provide the student with the necessary skills to develop programs for measurement, analysis, and instrumentation applications.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe and utilise principal concepts of object-oriented programming.
LO2 Describe the fundamental principles underpinning machine learning and artificial intelligence.
LO3 Develop software for machine learning tasks.
LO4 Develop object-oriented applications for laboratory and industrial usage.
LO5 Create custom class libraries for measurement, instrumentation and analysis applications.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
18481 PHYS7039 Prog for Instrum & Analysis
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Machine Learning and Artificial Intelligence
Supervised/unsupervised learning, classification, regression techniques (linear and polynomial), logistic regression, decision trees, support vector machines, ensemble methods, k-means clustering, principal component analysis, neural networks, deep learning, natural language processing, machine vision.
Machine Learning Workflow and Model Development
Jupyter notebooks. Data pre-processing: Visualisation, imputation, encoding, feature engineering, scaling, skew, multi-colinearity. Modelling: scikitlearn module, train/test splitting, cross-validation, overfitting/underfitting, hyperparameter tuning, model interpretation.
Object Oriented Programming
Encapsulation, inheritance, overloading and overriding, abstraction, polymorphism, classes, class libraries, design patterns.
Development of OOP Applications
Graphical User Interfaces (Tkinter, PyQt). Web applications (Django). Use of class libraries for data acquisition and instrument control. Efficient use of generative AI tools. Documentation. Testing.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Other % of Total Mark 25
Timing Week 4 Learning Outcomes 1
Assessment Description
SAQ & OOP Skills Evaluation
Assessment Type Other % of Total Mark 25
Timing Week 8 Learning Outcomes 2,3
Assessment Description
SAQ and Open Book Practical/Skills Evaluation
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 4,5
Assessment Description
Group Project e.g. development of GUI/web-based object-oriented application for purposes of interfacing to laboratory or industrial hardware, database management, data analysis/visualisation.

Submission to include some or all of the following: preparatory work, exemplar engagement, progress report(s), presentation, poster, interview, final report, code submission, reflective journal.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of module content Every Week 2.00 2
Lab Contact Delivery and application of module content e.g. object-oriented programming, machine learning, GUI development, hardware interfacing. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Study and homework Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of module content Every Week 2.00 2
Lab Contact Delivery & application of module content Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Study & homework Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Aurélien Géron. (2022), Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 4th. [ISBN: 1098125975].
Supplementary Book Resources
  • John Hughes. (2010), Real World Instrumentation with Python, 1st. O'Reilly Media, p.624, [ISBN: 0596809565].
  • Wes McKinney. (2022), Python for Data Analysis, O'Reilly Media, p.550, [ISBN: 109810403X].
This module does not have any article/paper resources
Other Resources