MATHS 1008  Mathematics for Information Technology I
North Terrace Campus  Semester 2  2017

General Course Information
Course Details
Course Code MATHS 1008 Course Mathematics for Information Technology I Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week Available for Study Abroad and Exchange Y Prerequisites SACE Stage 2 Mathematical Studies Assessment ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Dr Adrian Koerber
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.

Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to: Demonstrate understanding of concepts in discrete mathematics, probability and cryptography.
 Employ methods related to these concepts in a variety of applications.
 Apply logical thinking to problem solving in context.
 Use appropriate technology to aid problem solving.
 Demonstrate skills in writing mathematics.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
 informed and infused by cutting edge research, scaffolded throughout their program of studies
 acquired from personal interaction with research active educators, from year 1
 accredited or validated against national or international standards (for relevant programs)
all Critical thinking and problem solving
 steeped in research methods and rigor
 based on empirical evidence and the scientific approach to knowledge development
 demonstrated through appropriate and relevant assessment
2,3,4 
Learning Resources
Required Resources
Mathematics for Information Technology I student notes.Recommended Resources
 Ross, K. A. & Wright, C. R. B., Discrete Mathematics, Prentice Hall
 Johnsonbaugh, R., Discrete Mathematics (7th ed), Prentice Hall
 Goodman, R., An introduction to stochastic models, BenjaminCummings
 Ross, S., Introduction to probability models (7th ed), Academic Press
Online Learning
This course uses MyUni extensively and exclusively for providing electronic resources, such as lecture notes, assignment and tutorial questions, and worked solutions. Students should make appropriate use of these resources.
MyUni can be accessed here: https://myuni.adelaide.edu.au/
Students are also reminded that they need to check their University email on a daily basis. Sometimes important and timecritical information might be sent by email and students are expected to have read it. Any problems with accessing or managing student email accounts should be directed to Technology Services. 
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures to guide students through the material, tutorial classes to provide students with class/small group/individual assistance, and a sequence of written and online assignments to provide formative assessment opportunities for students to practice techniques and develop their understanding of the course.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 48 72 Tutorials 11 22 Assignments 11 55 Mid Semester Test 1 7 Total 156 Learning Activities Summary
The two topics of discrete mathematics and probability detailed below are taught in parallel, with two lectures a week on each. The tutorials are a combination of the two topics, pertaining to the previous week's lectures. Note that some sections only loosely fall into the categories of discrete mathematics or probability but are so listed to indicate the stream they are taught in.
Lecture Outline
Discrete Mathematics Sets and relations, equivalence relations, functions. (4 lectures)
 Logic, predicate caclulus. (2 lectures)
 Types of argument. (2 lectures)
 Switching circuits. (2 lectures)
 Graphs, trees, spanning trees, Kruskal's algorithm, binary search trees. (5 lectures)
 Mathematical induction. (3 lectures)
 Cryptosysytems, Caesar cipher, Hill cipher. (2 lectures)
 Elementary number theory. (2 lectures)
 Public key cryptography, the mathematics of the RSA algorithm. (2 lectures)
 Sample spaces, events, inclusionexclusion. (3 lectures)
 Conditional probablility and the product rule. (1 lecture)
 Probability trees, independent events, Bayes' Formula, Law of Total Probability. (2 lectures)
 Discrete random variables and probability distributions. (6 lectures)
 Counting techniques. (6 lectures)
 Linear homogeneous recurrence relations with constant coefficients. (6 lectures)
Tutorial 1: Sets, relations, Venn diagrams, simple probability.
Tutorial 2: Functions, 11, onto. Conditional probability.
Tutorial 3: Propositions, truth tables. Probability tree diagrams.
Tutorial 4: Types of argument, negation. Bayes' Theorem, Law of Total Probability.
Tutorial 5: Boolean expressions, circuit diagrams. Permutation, combinations, counting arguments.
Tutorial 6: Mathematical induction, recursive definitions. Partitions. Binomial probability.
Tutorial 7: Graphs, trees, paths. Binomial proability distribution.
Tutorial 8: Kruskal's algorithm. Binary search trees. Mode, median. Normal distribution.
Tutorial 9: Binary search trees. Caesar cipher. Markov chains.
Tutorial 10: Hill cipher, elementary number theory. Recurrence relations.
Tutorial 11: Congruence, Fermat's theorem. Inhomogeneous recurrence relations.
Note: Precise tutorial content may vary due to the vagaries of public holidays.

Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
 Assessment must encourage and reinforce learning.
 Assessment must enable robust and fair judgements about student performance.
 Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
 Assessment must maintain academic standards.
Assessment Summary
Assessment Task Task Type Weighting Learning Outcomes Assignments Formative 15% all Mid Semester Test Summative and Formative 15% 1,2,3 Exam Summative 70% 1,2,3,5 Assessment Related Requirements
An aggregate score of 50% is required to pass the course. Furthermore students must achieve at least 45% on the final examination to pass the course.Assessment Detail
Handin (written) assignments are due every week, the first is released in Week 1 and due in Week 3.
Tutorials are weekly beginning in Week 2.
The Mid Semester Test occurs in your enrolled computer lab in Week 8.
Precise details of all of these will be provided on the MyUni site for this course.Submission
 All written assignments are to be submitted at the designated time and place with a signed cover sheet attached.
 Late assignments will not be accepted without a medical certificate.
 Written assignments will have a one week turnaround time for feedback to students.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 149 Fail P 5064 Pass C 6574 Credit D 7584 Distinction HD 85100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
Replacement and Additional Assessment Examinations (R/AA Exams)
Students are encouraged to read the University's R/AA exam information on the University’s Examinations webpage here:
http://www.adelaide.edu.au/student/exams/modified/replacement/
In this course Additional (Academic) exams will be granted to those students who have obtained a final mark of 40–49%. 
Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including ongoing engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

Student Support
 Academic Integrity for Students
 Academic Support with Maths
 Academic Support with writing and study skills
 Careers Services
 International Student Support
 Library Services for Students
 LinkedIn Learning
 Student Life Counselling Support  Personal counselling for issues affecting study
 Students with a Disability  Alternative academic arrangements
 YouX Student Care  Advocacy, confidential counselling, welfare support and advice

Policies & Guidelines
This section contains links to relevant assessmentrelated policies and guidelines  all university policies.
 Academic Credit Arrangements Policy
 Academic Integrity Policy
 Academic Progress by Coursework Students Policy
 Assessment for Coursework Programs Policy
 Copyright Compliance Policy
 Coursework Academic Programs Policy
 Elder Conservatorium of Music Noise Management Plan
 Intellectual Property Policy
 IT Acceptable Use and Security Policy
 Modified Arrangements for Coursework Assessment Policy
 Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
 Student Experience of Learning and Teaching Policy
 Student Grievance Resolution Process

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