BACKGROUND AND RATIONALE
Information Retrieval module introduces learners to information retrieval. The module starts with explaining the search and analysis of unstructured data extracted from web search engines, text classification and text clustering. It
proceeds to explain the various systems for collecting, indexing and searching documents. It explains the storage, organisation and access of information, as implemented in various large-scale online systems and services. The further explains the latest advancements, which underpin web search engines, text classification, text clustering and information extraction from structured and unstructured data. It explains the structure and the functionality of various systems for collecting, indexing and searching documents and ends with explaining the process of developing an advanced information retrieval system of industrial strengths.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Analzse and compare current approaches to requirements
management within a development environment.
– Assess the impact of stakeholders and organizational culture on the
development of effective requirements and system development.
– Relate issues associated with risk, quality, and
Legal/Social/Ethical/Professional (LSEPI) to a practical scenario.
UNIT 1: Key concepts in Information Retrieval
UNIT 2: Boolean Retrieval, processing Boolean queries
UNIT 3: Dictionaries in Information Retrieval
UNIT 4: Index Construction
UNIT 5: Scoring and term weighting
UNIT 6: Evaluation in Information Retrieval
UNIT 7: XML Retrieval
UNIT 8: Probabilistic Retrieval
UNIT 9: Information Retrieval Using Machine Learning
UNIT 10: Vector Space and Support Vector Machines
UNIT 11 Flat and Hierarchical Clustering
UNIT 12: Web search engines, web crawling and indexing
ASSESMENT
Assignment 1 | 15% |
Assignment 2 | 15% |
Final exam | 70% |
Total | 100% |
PRESCRIBED READINGS:
Allen B. Downey (2017). Think Data Structures: Algorithms and Information Retrieval in Java. Sebastopol: Published by O’Reilly Media Ricardo Baeza-Yates and Berthier Ribeiro-Neto (2011). Modern Information Retrieval: The Concepts and Technology Behind Search. Essex: Pearson Education
RECOMMENDED READINGS
Cheng Xiang Zhai and Sean Massung (2016). Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining. Virginia: Association for Computing Machinery and Morgan & Claypool Publishers
BACKGROUND AND RATIONALE
IT Security and Privacy Risk Management module introduces learners to IT
security and privacy risk management. The module begins by giving a security
and privacy overview. It explains the issues, threats and their impact on a
business environment. It explains issues relating to security and privacy risks.
Highlights security and privacy incidents and their management including
detection and monitoring. Explains risk management. Explains the process of
identifying and analysing various security and privacy risks then ends with
explaining the process of security and privacy evaluation.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Critically evaluate various security and privacy threats and their impact on an organisation,
– Critically investigate approaches to managing and mitigating the security and privacy threats on organisations.
– Critically evaluate the desired regulations, laws and standards that may need to be complied with to ensure the security and privacy of information systems of the organisation.
– Apply various approaches for the evaluation of security risks, e.g., audit.
COURSE CONTENTS
UNIT 1: Security and privacy overview.
UNIT 2: Issues, threats and their impact on a business environment.
UNIT 3: Security and privacy risks.
UNIT 4: Security and privacy incidents and their management including
detection and monitoring.
UNIT 5: Risk management.
UNIT 6: Identification and analysis of various security and privacy risks.
UNIT 7: Security and privacy evaluation.
UNIT 8: Various frameworks (e.g., NIST, C2MS), standards (e.g., ISO27001,
ISO27002), laws (e.g., Cyber Security Law) and regulations (e.g.,
GDPR).
UNIT 9: Security audit management.
ASSESMENT
Assignment 1 | 15% |
Assignment 2 | 15% |
Final exam | 70% |
Total | 100% |
PRESCRIBED READINGS:
Raymond Pompon (2016). IT Security Risk Control Management: An Audit
Preparation Plan. Seattle: Raymond Pompon
Bell G. Raggad (2010). Information Security Management: Concepts and
Practice. Boca Raton, Florida: Taylor & Francis Group
RECOMMENDED READINGS:
Barry L. Williams (2013). Information Security Policy Development for
Compliance: ISO/IEC 27001, NIST SP 800-53, HIPAA Standard, PCI DSS V2.0, and
AUP V5.0. 2013. Boca Raton, Florida: Taylor & Francis Group.
Evan Wheeler (2011). Security Risk Management: Building an Information
Security Risk Management Program from the Ground Up. Massachusetts:
Elsevier Inc.
BACKGROUND AND RATIONALE
Machine Learning module introduces learners to machine learning. The module begins by explaining key Machine Learning principles and algorithms. It explains the theoretical background of Machine Learning and its numerous
applications in science and engineering. Explains the Wide range of approaches machine learning is presented. It explains the performance and suitability of the various types of tasks and data. Explains the concept of supervised and unsupervised algorithms and their applications in real-world environments. It further explains and evaluates the optimal approaches to machine learning and ends with explaining the importance of data and their features and properties.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Know and critically analyse the recent advancements in Machine Learning and its ability to solve a wide range of real-world problems.
– Conduct research on the analysis and evaluation of various advanced Machine Learning algorithms, assess and compare their functionality and performance.
– Know and apply knowledge to design and develop a sophisticated software system, which solves a real-world domain-specific problem by utilising an appropriate machine learning approach.
COURSE CONTENTS
UNIT 1: Main concepts in Machine Learning.
UNIT 2: Programming Languages and Software Tools.
UNIT 3: Pre-processing Data.
UNIT 4: Approaches to Machine Learning.
UNIT 5: Supervised Learning, training and testing data, active learning, classification, regression.
UNIT 6: Model training, overfitting and under-fitting.
UNIT 7: Unsupervised Learning, unsupervised networks, probabilistic methods.
UNIT 8: Reinforcement Learning, value function, direct policy.
UNIT 9: Model Evaluation and Improvement.
UNIT 10: Algorithm Chains and Pipelines.
UNIT 11: Dimensionality Reduction, principal component analysis, non-linear
dimensionality reduction.
ASSESMENT
Assignment 1 | 15% |
Assignment 2 | 15% |
Final exam | 70% |
Total | 100% |
PRESCRIBED READINGS:
Manohar Swamynathan (2017). Mastering Machine Learning with Python in Six
Steps: A Practical Implementation Guide to Predictive Data Analytics Using
Python. Bangalore: Manohar Swamynathan
Andreas C. Müller and Sarah Guido (2017). Introduction to Machine Learning
with Python: A Guide for Data Scientists. Sebatopol: O’Reilly Media
RECOMMENDED READINGS:
Rudolph Russell (2018). Machine Learning: Step-by-Step Guide to Implement
Machine Learning Algorithms with Python. Rudolph Russell
Aurélien Géron (2017). Hands-On Machine Learning with Scikit-Learn and
TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
Sebatopol: O’Reilly Media
BACKGROUND AND RATIONALE
Database Management and Administration module introduces learners to database management and administration. The module begins by explaining the concept of database architecture; explains the process of creating the database; explains the process of managing database instance; explains the process of managing database storage structures; explains the process of managing transactional processing and locking mechanism; explains database security; explains the process of monitoring the database and using the advisors; explains the backup and recovery concepts; and ends with explaining the process of Investigating, reporting, and resolving problems.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Critically evaluate the concepts and tools of the database
management systems.
– Know and apply the knowledge to design, build and manage various
database structures based on the specified business requirements.
– Know and identify essential database security issues and demonstrate
ability to solve them in the timely manner.
– Develop critical awareness of issues relating to database management
and practical skills to solve common database management problems
COURSE CONTENTS
UNIT 1: Exploring the database architecture;
UNIT 2: Creating the database;
UNIT 3: Managing the database instance;
UNIT 4: Managing database storage structures;
UNIT 5: Managing transactional processing and locking mechanism;
UNIT 6: Database security;
UNIT 7: Monitoring the database and using the advisors;
UNIT 8: Backup and recovery concepts;
UNIT 9: Investigating, reporting, and resolving problems.
ASSESSMENT
PRESCRIBED READINGS:
Matthew Helmke etal. (2019). Ubuntu Unleashed. Pearson Education
Carlos Coronel and Steven Morris (2017). DATABASE SYSTEMS: Design,
Implementation, and Management 12e. Massachusetts: Cengage Learning
RECOMMENDED READINGS:
Peter Rob and Carlos Coronel (2009). Database Systems: Design,
Implementation, and Management. Massachusetts: Thomson Course
Technology