2018 Semester 2 - Unit Information for MATH1933: Multivariable Calculus and Modelling (SSP)
Classes
The Special Studies Program is formally attached to MATH1923 with one additional seminar and a special tutorial. In particular you attend:
- The same lectures as MATH1923;
- Seminar: Tuesday 4pm, New Law Room 346
- Tutorial: Thursday 10am, Carslaw Room 359 (replaces your MATH1923 tutorial).
The special topics in the seminar are independent of MATH1923. There will be three different topics presented by different lecturers.
The tutorial will be on the material in MATH1923
You will need all material for MATH1923. Check the MATH1923 Web page or the MATH1923 Unit Information Sheet on how to get these.
Special Topics
There will be three topics from various fields of mathematics and statistics presented by different lecturers:
Note that the order of topic will need to be confirmed
- Weeks 2–5: Oded Yacobi, Euler’s Formula in Graph Theory and Topology
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Euler's formula is a beautifully simple statement about connected planar graphs: (number of vertices)-(number of edges)+(number of faces)=2. We will prove this formula and present some surprising applications, including the Sylvester-Gallai Theorem: given at least two points in the plane, either they all on a line or there exists a line which contains exactly 2 of the points. We will then use Euler's formula to define the Euler characteristic of surfaces, offering a glimpse of the beauty of algebraic topology.
- Weeks 6–9: Rachel Wang, A brief introduction to probabilistic graphical models
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Probabilistic graphical models provide a succinct framework for representing complex dependencies among random variables. We take a first dip into this field by studying how joint probabilities can be represented using directed and undirected graphs and discussing algorithms for performing simple inferential tasks. Graphical models include many popular statistical models such as hidden Markov models, Gaussian graphical models, as special cases and have been the focus of research in numerous statistical and computational fields ranging from machine learning, artificial intelligence, signal processing, bioinformatics, to statistical physics.
- Weeks 10–13: Florica Cîrstea, Fractals
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Many objects in nature can be modelled by fractals. Fractal sets or images have the property that if we look at them under a microscope, using larger and larger magnifications, we continue to see similar features at all scales. Examples of fractals include biology (blood vessel patterns, structure and development of plants), physics (statistical mechanics, dynamical systems), computer science (image compression, compression for multimedia), engineering (image encoding, antennae, signal processing), and chemistry (pattern-forming alloy solidification). We will look at a number of fractals and discuss how to generate them. We will also study some of their surprising properties.
Assessment
The final mark in MATH1933 is determined as follows:
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All assessments for MATH1923. For more detail look at the MATH1923 unit information sheet. This counts 80% of the total mark.
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Three equally weighted assignments, one on each special topics in the seminar. The three assignments count 10% towards the total mark. (bettermark applies if all assignments are handed in, otherwise no bettermark)
Assignment submission dates: Fri 7 Sep (first topic), Fri 12 Oct (second topic), Fri 2 Nov (third topic)
All assignments have to be submitted through the LMS to be passed through the text matching software Turnitin (scanned handwritten assignments are fine, there is absolutely no need to spend time on typesetting!).
- One mark for every seminar participation, up to a total of 10. The total counts 10% towards the total mark. (no bettermark!)
Under exceptional circumstances you may be allowed to attend a MATH1923 tutorial. Contact the coordinator Daniel Daners for permission to do so.
Outcomes
The outcomes for MATH1933 include all outcomes for MATH1923 as well as the following: The student completing MATH1933 will
- gain an appreciation of a diverse range of mathematical problems and applications through participating in class discussions and the completion of assignments.
- be able to grasp new mathematical concepts beyond routine methods and calculations.
Additional Information
Attendance:
Unless otherwise indicated, students are expected to attend a minimum of 80% of timetabled activities for a unit of study, unless granted exemption by the Associate Dean.
For some units of study the minimum attendance requirement, as specified in the relevant table of units or the unit of study outline, may be greater than 80%.
The Associate Dean may determine that a student has failed a unit of study because of inadequate attendance.
Further details are available from the Science Undergraduate Handbook 2018 or the Science Postgraduate Handbook 2018.
Online Components:
This unit of study requires regular use of the University’s Learning Management System (LMS). This could be either Blackboard or Canvas. Internet access is required to use the LMS.
Assessment Submission:
Assessment tasks must be submitted by the due date. Submission will be online through the LMS unless instructed otherwise.
Compliance Statement
All students must submit a signed statement of compliance with each piece of work submitted to the University for assessment, presentation or publication. A statement of compliance certifies that no part of the work constitutes a breach of the Academic Honesty in Coursework Policy 2015:
https://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/254&RendNum=0.
This will be completed as part of the Turnitin assignment submission.
Late Submissions
Work not submitted by the due date is not accepted.
Academic Dishonesty and Plagiarism
Academic honesty is a core value of the University. Therefore, all students are required to act honestly, ethically and with integrity. Academic dishonesty is defined as any dishonest or unfair action taken in order to gain academic advantage. It also includes knowingly assisting another student to do this.
The University will not tolerate academic dishonesty or plagiarism, and will treat all allegations of academic dishonesty and plagiarism seriously.
Plagiarism is defined as presenting another persons work as ones own by presenting, copying or reproducing it without appropriate acknowledgement of the source.
Plagiarism includes presenting work for assessment, publication, or otherwise, that includes:
- phrases, clauses, sentences, paragraphs or longer extracts from published or unpublished work (including from the internet) without appropriate acknowledgement of the source; or
- the work of another person, without appropriate acknowledgement of the source and in a way that exceeds the boundaries of legitimate co-operation.
Further information is available in the Academic Honesty in Coursework Policy 2015:
https://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/254&RendNum=0.
Similarity Detection Software
Students should be aware that the University has authorised and mandated the use of the text-based similarity detecting software called Turnitin for all text-based written assignments. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works, and assignments that have previously been submitted for analysis.
Further information regarding plagiarism detection is available in the Academic Honesty in Coursework Policy 2015:
https://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/254&RendNum=0.
Academic Honesty Education Module (AHEM)
All students commencing their study at the University of Sydney are required to complete the Academic Honesty Education Module. You will find the AHEM in your Learning Management System.
Special Consideration
In the event of serious illness or misadventure which affects your preparation or performance in an assessment task, you may be eligible for Special Consideration.
You should not submit an application for Special Consideration or Special Arrangements for this unit of study
- if you are absent from a tutorial and there is no assessment associated with the missed tutorial, or
- if you miss a quiz, since the better mark principle applies.
The assessment category for the assignments is “Submitted Work”.
Student Feedback: The Unit of Study Survey
At the completion of each Unit of Study, students are asked via email to complete an online survey to provide feedback on their experiences in that Unit of Study. This feedback is invaluable when reviewing curriculum design and implementation styles.
University Work, Health and Safety Policy:
We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.
General Laboratory Safety Rules
- No eating or drinking is allowed in any laboratory under any circumstances
- A laboratory coat and closed-toe shoes are mandatory
- Follow safety instructions in your manual and posted in laboratories
- In case of fire, follow instructions posted outside the laboratory door
- First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service.
For more details please refer to Emergencies and safety on campus see Emergencies and safety on campus