MACHINE LEARNING AND DATA MINING - 2021/2
Module code: COMM055
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
Machine Learning and Data Mining incorporate a large number of ways in which datasets with a variety of characteristics can be processed in order to provide for new insights and understanding. Through treatment of the principles and fundamental requirements for ML/DM, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent data treatments, some of which lead to deep learning approaches. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
TAMADDONI NEZHAD Alireza (Computer Sci)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I400
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Co-requisite: COMM054, Data Science Principles and Practices
Indicative content includes: Data Mining EDISON descriptors: KU1.01.02-KU1.01.05, KU1.01.08, KU1.03.01, KU1.03.04, KU1.03.05, KU1.03.06)
• Statistical paradigms (regression, time series, dimensionality, clusters)
• Probabilistic representations (causal networks, Bayesian analysis, Markov nets)
• Frequentist and Bayesian statistics
• Probabilistic reasoning
• Performance analysis
• Data mining and knowledge discovery
• Anomaly Detection
• Time series analysis
• Feature selection, Apriori algorithm Text Data Mining EDISON descriptors: KU1.04.02
• Data mining and text analytics Predictive Analytics EDISON descriptors, KU1.05.01
• Predictive modeling and analytics Machine Learning EDISON descriptors: KU1.02.01- KU1.02.05, KU1.02.08
• Machine Learning theory and algorithms
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforced learning
• Classification methods
• Artificial Intelligence Appropriate software will be used for exploration of the above.
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAMINATION (2 HOURS)||60|
The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.
Thus, the summative assessment for this module consists of:
• A coursework involving a report comprising the comparative evaluation of machine learning and data mining methods when applied and adapted, using common software tools to specified datasets, with documentation of key insights derived. This will evaluate LOs 1, 2, 3, and have a deadline in or near to week 11.
• An examination assessing both principles and practices of Machine Learning and Data Mining. This will evaluate LOs 1, 2, 3, 4. Formative assessment Students will be guided to work on weekly tasks through lab exercises, the solutions to which will provide for feedback on understanding and practice. Labs and feedback will then support the coursework. Individual feedback on the coursework will be given as soon as possible before the exam in order to feed forward to the exam.
- This module aims to:
elaborate, demonstrate, and apply a variety of machine learning and data mining approaches for the treatment of datasets of various kinds, and in ways which may sometimes be considered artificially intelligent.
|001||On successful completion of this module, students will be able to: Select most appropriate statistical techniques and model available data to deliver insights (LODA.02 L2)||CKPT|
|002||Effectively use variety of data analytics techniques, from Machine Learning (including supervised, unsupervised, semi-supervised learning) and Data Mining for complex data analysis through the whole data lifecycle (DSDA01 [refined])||CPT|
|003||Analyze available data sources and define tools that work with complex datasets (LODA.03 L2 [refine])||CPT|
|004||Design and evaluate analysis techniques and tools to discover new relations (LODA.01 L3)||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 106
Lecture Hours: 22
Laboratory Hours: 22
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.
The learning and teaching methods include: 11 teaching weeks with each week comprising:
2 hour lectures, to convey and discuss the key concepts and principles
2 hour lab sessions, to put key concepts and principles into practice
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: COMM055
Programmes this module appears in
|Data Science MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.