| Module Title |
Foundations of Statistical Analysis & Machine Learning |
| Module Code |
CSC1181 |
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Faculty |
Engineering & Computing |
School |
Computing |
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NFQ level |
9 |
Credit Rating |
7.5 |
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Description
This module aims to review and complement foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems. This module will cover introductory topics and conventional algorithms needed to understand the field of Machine Learning. It will prepare students to understand and distinguish between main groups of methods used in Machine Learning, supervised and unsupervised, and how and when they are applicable. Key topics will include Regression, Decision Trees, Naive Bayes, Neural Networks and Clustering.
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Learning Outcomes
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 36 | Principles and methods | | Tutorial | 12 | Examples | | Assessment Feedback | 56 | Practical application/formal analysis | | Independent Study | 83.5 | Reading, understanding, applying concepts and reviewing examples |
| Total Workload: 187.5 |
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| Section Breakdown | | CRN | 12208 | Part of Term | Semester 1 | | Coursework | 50% | Examination Weight | 50% | | Grade Scale | 40PASS | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Marija Bezbradica | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| In Class Test | Supervised Lab exam | 20% | Sem 2 End | | Laboratory Portfolio | Three supervised lab submissions assessing the material covered in the previous weeks each comprising some independent coding work and some commentary. | 30% | As required | | Formal Examination | End of Module Formal Exam | 50% | End-of-Semester |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and Learning Activities
Topic 1
Summary Statistics, Conditional Probability, Data Types
Topic 2
Statistical Distributions
Topic 3
Hypothesis Testing and Confidence Intervals
Topic 4
Linear Regression and Logistic regression
Topic 5
Naïve Bayes, Decision Tree
Topic 6
Basics of Neural Networks and Perceptron
Topic 7
Categorical Variables, Regression Problem, hyperparameters
Topic 8
Introduction to feature Engineering – Clustering/ K-means clustering, Principal Component Analysis – Dimensional
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Indicative Reading List
Books:
- John D. Kelleher,Brian Mac Namee,Aoife D'Arcy: 2015, Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press, 619, 9780262029445
- Trevor Hastie, Robert Tibshirani, Jerome Friedman: 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2, 0387848576
- Tom Michael Mitchell: 1997, Machine Learning, McGraw-Hill Science/Engineering/Math, 414, 9780070428072
- Michael J. Crawley: 2005, Statistics, John Wiley & Sons, 348, 0470022981
- Thomas H. Davenport,Jeanne G. Harris: 2007, Competing on Analytics, Harvard Business Press, 243, 1422103323
- Thomas H. Davenport,Jeanne G. Harris,Robert Morison: 2010, Analytics at Work, Harvard Business Press, 231, 1422177696
- Sam L. Savage: 2009, The Flaw of Averages, Wiley, 0, 0471381977
Articles:
None |
Other Resources
None |
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