Module

This module builds on fundamentals of compound interest introduced in CMPC1F07 to show how the key concepts introduced can be used in practical financial applications for Actuarial Science or Business Statistics students. THIS MODULE IS RESERVED FOR ACTUARIAL SCIENCE AND BUSINESS STATISTICS STUDENTS.

Teaching Resources:

 This is the closest in level and scope to what we cover. Should be seen more as an aid than a strict unit text:

• Gersting,J. L., Mathematical Structures for Computer Science, Freeman.
• Lipschutz,S., Discrete Mathematics, McGraw-Hill.
• Lipschutz,S., Probability, McGraw-Hill.
• Mathews,J. H.,  Numerical Methods for Mathematics, Science and Engineering,, Prentice Hall.
• Stroud,K.A., Engineering Mathematics

 

 Submission

Written coursework should be submitted by following the standard CMP practice. Students are advised to refer to the Guidelines and Hints on Written Work in CMP.

Deadlines

Coursework should be submitted before 23:59 on the deadline day. Paper copies can be submitted via the Hub drop boxes up to 22.00  in the LTS Hub, and there will be a ‘late box’ in the Library for submissions between 22.00 and midnight.

If coursework is handed in after the deadline day or an agreed extension:
 

 

Work submitted	Marks deducted
On the day following the due date	10 marks
On either the 2nd or 3rd day after the due date	20 marks
On the 4th day after the due date and before the 20th day after the due date	All the marks the work merits if submitted on time (ie no marks awarded)
After 20 working days	Work will not be marked and a mark of zero will be entered

 

All extension requests will be managed through the LTS Hub. A request for an extension to a deadline for the submission of work for assessment should be submitted by the student to the appropriate Learning and Teaching Service Hub, prior to the deadline, on a University Extension Request Form accompanied by appropriate evidence. Extension requests will be considered by the appropriate Learning and Teaching Service Manager in those instances where (a) acceptable extenuating circumstances exist and (b) the request is submitted before the deadline. All other cases will be considered by a Coursework Coordinator in CMP.

Plagiarism

Plagiarism is the copying or close paraphrasing of published or unpublished work, including the work of another student; without due acknowledgement. Plagiarism is regarded a serious offence by the University, and all cases will be investigated. Possible consequences of plagiarism include deduction of marks and disciplinary action, as detailed by UEA's Policy on Plagiarism and Collusion.

Module Objectives:

  Module specific:

• To introduce the mathematics of counting and arrangements
• To develop the theory and practice calculus
• To introduce linear algebra and its applications
• To develop principles and applications of probability theory
• To introduce complex numbers

Transferable skills:

• To gain knowledge in a range of mathematical skills that are relevant to various areas of computer science and its applications.
• To develop a methodical approach to problem solving using mathematical techniques and theory.

Learning Outcomes:

On completion of this module students should be able to:

• Solve problems that involve calculus
• Understand linear algebra and its applications
• Understand and use probability theory
• Solve problems that involve permutations and combinations
• Understand and use complex numbers

Teaching Approach:

Syllabus:

The course consists of 2 hours of lectures per week together with a 2 hour exercise class per week.

Total Hours: 44

Lectures: 22, Content: (with provisional weekly schedule)

1. Permutations and combinations (3 lectures)
   Combinations. Permutations. Combinations with repetition. Binomial theorem. Applications in computing.
2. Sequences and series (3 lectures)
   APs, GPs, limits, tests for convergence, divergence.
3. Calculus (4 lectures)
   Limits. Differentiation and integration, differential equations. Applications of differential and integral calculus.
4. Probability (5 lectures)
   Independent events. Calculating probabilities over trees of events. Bayes' theorem. Random variables. Simple distributions. Independent random variables. Markov chains. Basic statistics.
5. Linear algebra (4 lectures)
   Matrices and vectors. Operations on vectors and matrices. Transformations in terms of matrices. Elementary row operations. Determinants. Linear independence, Eigenvectors, applications in computing.
6. Complex Numbers (2 lectures)
   Introduction, Addition and multiplication, Graphical representation, polar form, exponential form.

Workshops: 11; hours: 22; content: 
Weekly workshops based on exercise sheets for that week's lectures

Laboratory work: 0 hours

Method of Assessment: