National University of Ireland, Galway Science without Borders

More advanced coverage of Next Generation Technology topics including: Digital Media and Games Development. Medical and Bioinformatics. Acquisition of Biosignals, Lossy and Lossless Data Compression Techniques, Analysis and Classification of Biosignals. Biostatistical Methods. Energy Informatics. Computational Informatics. Enterprise Systems.

Transformations. Projections. Rendering Standards. Edge detection. Shape contours. Segmentation. Object recognition. Industrial applications.

Introduction. Distributed Systems. Enabling Technology. High-Bandwidth Networks. Distributed Systems. ANSA/ISA Architecture. Open Distributed Processing. Distributed Application Platforms. Transparency. Reliability. Computer-Supported Co-operative Work. Human-Computer Interaction. Human-Interaction. Groupware. Multimedia. Hypertext. Security. Asynchronous Groupware. E-mail. Structured Messages. Co-operative Hypertext Systems. Synchronous Groupware. Seeheim Model. WYSISIS. Multi-user Interfaces. Group-Enabled Applications. Shared Window Systems. Desktop Conferencing. Computer-Supported Meetings. Media Spaces. Telework. Telepresence. Commercial Groupware examples. Research Trends.

Software Project Management. Metrics and Behaviour. Measuring software projects. Project costings and projections. Software Quality Assurance: ISO and CMM Model. Object-oriented Analysis and Design. Methodology review, detailed instruction in one particular object-oriented methodology. Software Engineering: The Past, Present and Future.

AI History and Applications. Predicate Calculus, Search Strategies, Production Systems. Review of primary languages; Prolog and LISP. Rule-Bases Expert Systems, Knowledge Representation and Natural Language. Review of Automated Reasoning. Machine Learning and Advanced AI Techniques.

Data Mining, Data Warehousing, Data Mining, Data Warehousing Retrieval, Filtering, Extraction, Classification. Text Retrieval. Text Retrieval Models: Boolean, Statistical, Linguistic. Lexical Analysis, Stemming Algorithms Vector Space Model, Latent Semantic Indexing, Semantic Networks, Connectionist approaches. Multi-Media Retrieval. Evaluation: Precision/Recall Measures. Machine Learning, Relevance Feedback. Collaborative Retrieval.

The nature of systems thinking. The art of problem solving. The scientific method. System methodologies. Systems Dynamics. Soft systems methodology. Total systems intervention. Case studies.

This module introduces Object-oriented design and covers topics such as: Encapsulation and information-hiding, the separation of behavior and implementation, classes and subclasses, Arrays, Composition, Inheritance and Polymorphism.

This module progresses the students' study of computer systems with a focus on the analysis and design of software systems and the stakeholders involved. Students will develop specific systems analysis design skills (Software Development Lifecycle techniques) and reflect on the social and ethical issues associated with systems design.

This module provides students with an overview of the key components and systems in analog and digital electronics. The underlying principles of semiconductor materials, binary numbers, Boolean logic, and sequential logic, form the platform for understanding of higher level device/circuit design and performance. The functionality of some of the more common and useful specific electronic devices is explored. We explain the integration of such components into higher-level microprocessors, and study the instructions sets used to programme them.

Techniques and applications of computational physics are described. In accompanying practical classes, programs are written in a modern computer language to investigate physical systems, with an emphasis on dynamical problems.

1. Name and describe the main tasks of an operating system.

2. Explain the concept and purpose of a process in an operating system.

3. Represent the life cycle of a process in a diagrammatical fashion.

4. Describe and compare various scheduling strategies.

5. Explain and implement a queue data structure.

6. Apply a semaphore as a tool in concurrent programming.

7. Explain the necessary conditions for deadlock.

8. Describe and apply an algorithmic strategy for deadlock detection.

Code	Module Title	Semester	ECTS	Examination Arrangements
MA204	Discrete Mathematics	1	5	Two hour examination

This course deals with elementary enumeration, permutations, combinations, and graphs including eulerian and Hamiltonian graphs. On successful completion of this module the learner should be able to:

1. Distinguish between orderings (permutations) and subsets (combinations).

2. Count the size of unions and intersections of sets and solve elementary recurrences.

3. Define and apply Binomial and multinomial coefficients to enumeration problems.

4. Use tree graphs for enumeration.

5. Use trees to write algebraic expressions in Polish and Reverse Polish notation.

6. Define the notion of graph, eulerian, Hamiltonian, bipartite and tree graphs.

7. Define the notion of graph colourings and applications to scheduling problems.

1. Solve some definite integrals via reduction formulas.

2. Calculate volumes of revolution in straightforward instances.

3. State the definition of the hyperbolic functions and their inverses and properties of these functions.

4. Define the notion of limit of a sequence and be able to apply the comparison, ratio and root tests, and the integral test for convergence of series.

5. Evaluate improper integrals and so-called p-integrals.

6. Solve linear 1st and 2nd order differential equations.

Code	Module Title	Semester	ECTS	Examination Arrangements
MA217	Statistical Methods for Business	1	5	Two hour examination

This module demonstrates methods in statistical inference with applications in Business, Finance, Marketing and Economics. This is a first course in statistical inference covering sampling distributions, construction of confidence intervals and hypothesis testing, and communication of results of analysis applied to a range of business problems. Students must have completed an introductory course in descriptive statistics and probability similar to the content of MA109 Statistics for Business.

On successful completion of this module the learner should be able to:

1. Define and identify in applications, basic terms: experimental unit, population, sample, variables and their types, parameter, statistic, descriptive statistics and inferential statistics.

2. Define the term standard error and define, discuss and identify common sampling distributions and define and apply the Central Limit Theorem in the context of the sampling distribution of the mean and the sampling distribution of the proportion of successes. Discuss the sampling distribution of the mean for large and small samples and discuss and check any assumptions that apply in those cases.

3. Construct and interpret a confidence interval for a population mean for large and small samples. Discuss and check any assumptions that apply in doing so. Construct confidence intervals at varying levels of confidence and discuss the implications of changes in the confidence level and the sample size on the resulting interval.

4. Carry out a hypothesis test for a population mean for large and small samples. Discuss and check any assumptions that apply in carrying out the analysis. Define type I and type II error, the significance level, the test statistic, the power of the test and the p- value and interpret each of these terns in application. Complete the hypothesis test by either determining a rejection region for the test statistic, a rejection region for the sample estimate of the parameter, or a p-value. Identify and complete one and two tailed testing procedures.

5. Expand application of basic skills learned in constructing confidence intervals and carrying out hypothesis tests for inferring the value of a single population mean to other problems such as:

- Inference for comparing means of two populations (large and small samples), independent samples

- Inference for comparing means between two populations (large and small samples), paired samples

- Inference for comparing means of more than two populations using ANOVA

- Inference for a single population proportion of successes (large samples only) in a binomial experiment

- Inference for population proportions in a multinomial experiment, the e χ2 goodness of fit test

- Inference for comparing proportions of successes between two populations (large samples only), independent samples

- Inference for testing for an association between two qualitative variables in a population, the χ2 association/independence test

- Inference for testing for a linear relationship between two quantitative variables in a population, via simple regression analysis, including: estimation of the line of best fit, testing for significant population relationship by carrying out inference for the population slope parameter, and using the fitted line for estimation via a confidence interval or prediction interval as appropriate.

Code	Module Title	Semester	ECTS	Examination Arrangements
MA284	Discrete Mathematics	1	5	Two hour examination

This course covers topics in combinatorics, graph theory, and their applications. Section titles are as follows. Addition and multiplication principles; Permutations and combinations; Ordered and unordered selections with repetition; Inclusion and Exclusion; Graph isomorphism, subgraphs, connectedness; Travelling around a graph; Vertex colouring; Planarity; Trees. On successful completion of this module the learner should be able to:

1. Use the addition and multiplication principles correctly and appropriately.

2. Construct a combinatorial proof from first principles.

3. Distinguish between different combinatorial situations and use suitable techniques to solve the problems involved.

4. Identify inherent properties of graphs (planarity, Eulerian and Hamiltonian properties) from pictorial representations.

5. Apply graph-theoretic ideas to solve scheduling and optimisation problems.

6. Model relevant real-life problems using trees and solve them.

Code	Course	Semester	ECTS	Examination Arrangements
MA286	Analysis I	1	5	Two hour examination