Short Title:Applied Machine Learning
Full Title:Applied Machine Learning
Language of Instruction:English
Module Code:APML H4000
 
Credits: 5
Field of Study:Computer Science
Module Delivered in 9 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:SEAN MC HUGH
Module Description:The aims of the module are to: To instill an understanding of the foundations for advanced analytics and be able to apply the techniques underpinning the machine learning processes.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply data pre-processing and data exploration techniques in the context of the machine learning process
LO2 Demonstrate knowledge of machine learning techniques, their methods and application.
LO3 Determine the machine learning techniques and methods for particular scenarios
LO4 Evaluate the models produced using relevant performance metrics
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Laboratory Sample CA1: Explore a dataset and carry out appropriate data pre-processing techniques in preparing the data for the machine learning process 1 25.00 Week 4
Continuous Assessment Sample CA2: Given a particular dataset or problem apply appropriate data mining technique(s). Interpret results and produce findings 2,3,4 25.00 Week 9
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,4 50.00 End-of-Semester

TU Dublin – Tallaght Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Class Based instruction 2.00 Every Week 2.00
Laboratories Practicals/Workshops 2.00 Every Week 2.00
Independent Learning Reading/Study 4.00 Every Week 4.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Class/Lab Based instruction 4.00 Every Second Week 2.00
Independent Learning ReadingStudy 4.00 Every Week 4.00
Total Weekly Learner Workload 6.00
Total Weekly Contact Hours 2.00
 

Module Resources

Required Book Resources
  • Ian H. Witten, Eibe Frank, Mark A. Hall 2017, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 4 Ed., Morgan Kaufmann [ISBN: 0128042915]
  • Jason Brownlee 2017, Machine Learning Mastery With Python; Understand your Data, Create Accurate Models and Work Projects End to End, Machine Learning Mastery
Recommended Book Resources
  • Sarah Guido, Andreas C. Mueller 2016, Introduction to Machine Learning with Python, O'Reilly Media [ISBN: 1449369413]
  • Sebastian Raschka,‎ Vahid Mirjalili 2017, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow,, 2 Ed., Packt Publisihing [ISBN: 1787125939]
  • Daniel T. Larose, Chantal D.Larose 2014, Discovering Knowledge in Data, 2nd Ed., Wiley [ISBN: 978047090874]
  • Wes McKinney 2017, Python for Data Analysis, 2 Ed., O′Reilly [ISBN: 1491957662]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KCOMP_B (1 year add on) Bachelor of Science (Honours) in Computing 7 Mandatory
TA_KACTM_B Bachelor of Science (Honours) in Computing with Information Technology Management 7 Elective
TA_KCITM_B Bachelor of Science (Honours) in Computing with Information Technology Management - Year 4 ( Add on) 7 Elective
TA_KACOS_B Bachelor of Science (Honours) in Computing with Software Development 7 Elective
TA_KCOSD_B Bachelor of Science (Honours) in Computing with Software Development - Year 4 ( Add on) 1 Elective
TA_KITMG_B Bachelor of Science (Honours) IT Management (add On) 7 Elective
TA_KACOD_B Bachelor of Science (Hons) in Computing with Data Analytics 7 Mandatory
TA_KCODA_B Bachelor of Science (Hons) in Computing with Data Analytics (Add-On) 1 Mandatory
TA_KMLAI_B Certificate in Machine Learning & Artificial Intelligence 1 Mandatory