MFA501 Assessment 2 Brief Problem Sets Modules 6, 10 Page 1 of 5
Task Summary
In this assessment, you will be given a wide range of programming exercises to complete, which
equire using and applying mathematical concepts. These will be submitted in Module 6 (20%) and in
Module 10 (20%). This assessment is to be completed individually and you are to submit a zip file
including source code, debug, and release build and supporting documents for each problem set.
Context
This assessment activity assesses your skills in employing AI mathematical foundation to solve real-
world problems and scenarios. The assessment is made of two parts due in modules 6 and 10 over
the course of trimester.
Task Instructions
The programs that you submit should be free of warnings and e
ors.
You need to submit the source code and the executable format.
o Name the source code folder as:
MFA501_Assessment2_Week6_LastName_FirstName.zip
o MFA501_Assessment2_Week10_LastName_FirstName.zip
Your code should be structured and written with the best practices in the field of
programming.
There should be enough number of comments in the source files to show your
understanding of the program. Any third-part code should be appropriately attributed.
ASSESSMENT 2: PROBLEM SETS BRIEF
Subject Code and Title MFA501 Mathematical Foundations of AI
Assessment Problem Sets
Individual/Group Individual
Length
Learning Outcomes This assessment addresses the Subject Learning Outcomes outlined
at the bottom of this document.
Submission Due by 11:55pm AEST Sunday end of:
Module 6 (Week 6) (20%)
Module 10 (Week XXXXXXXXXX%)
Weighting 40%
Total Marks 100 marks
MFA501 Assessment 2 Brief Problem Sets Modules 6, 10 Page 2 of 5
After implementation and testing your programs, write a reflective report detailing the experience
of the development process. The report needs to be at least 500 words in length and include the
following sections:
Overview
Justifications and elaborations on the mathematical approaches and models used to solve
the cases study
Justifications and elaborations on the programing methods and practices used to implement
the mathematical approaches and models
What went right
What went wrong
What you are not sure about
Conclusion
Your problem sets should include the following elements and should be zipped prior to submission:
Release Build Zip: A release build executable must be zipped and included with the
submission. Ensure that project settings are set to Release when creating this build.
Source Code Zip: All relevant source code files and project files must be zipped and included
with the submission
Reflective report: PDF or Word
Naming & File structure for the zip file (should be done for all problem sets) .
o MFA501_Assessment2_Set1_Release Build_LastName_FirstName.zip
o MFA501_Assessment2_Set1_Source_LastName_FirstName.zip
o MFA501_Assessment2_Set1_report_LastName_Firstname.pdf or .docx
Make sure to submit Problem Set 1 by Sunday 11:55pm Module 6
Make sure to submit Problem Set 2 by Sunday 11:55pm Module 10
Submission Instructions
This assessment task is due in two stages throughout the trimester as outlined above. Please submit
your completed assessments via the Assessment link in the main navigation menu in MFA501
Mathematical Foundations of AI. The Learning Facilitator will provide feedback via the Grade Centre
on Blackboard. Feedback can be viewed in My Grades
MFA501 Assessment 2 Brief Problem Sets Modules 6, XXXXXXXXXXPage 3 of 5
Assessment 2 Ru
ic
Assessment
Attributes
Fail
(Yet to achieve
minimum standard)
0-49%
Pass
(Functional)
50-64%
Credit
(Proficient)
65-74%
Distinction
(Advanced)
75-84%
High Distinction
(Exceptional)
85-100%
Work demonstrates the
knowledge and
understanding of linear
alge
a in AI
20%
Little or no knowledge of
linear alge
a in AI.
The calculations and codes
are mostly inco
ect.
Acceptable but further work is
equired to show the
knowledge of linear alge
a in
AI. The calculations and codes
are mostly co
ect with
occasional inaccuracies and
e
ors.
Good level of knowledge
about linear alge
a in AI.
The calculations and codes
are co
ect with no e
ors.
The codes are not efficient
for the case study.
Very good but not thorough
knowledge about linear
alge
a in AI. The
calculations and codes are
accurate. The codes are
occasionally not efficient.
Excellent and thorough
understanding of linear
alge
a in AI. The
calculations and codes are
accurate and completely
e
or-free. The most
efficient implementations
are given.
Work demonstrates the
knowledge and
understanding of
calculus in AI
20%
Little or no knowledge of
calculus in AI.
The calculations and codes
are mostly inco
ect.
Acceptable but further work is
equired to show the
knowledge of calculus in AI.
The calculations and codes
are mostly co
ect with
occasional inaccuracies and
e
ors.
Good level of knowledge
about calculus in AI. The
calculations and codes are
co
ect with no e
ors. The
codes are not efficient for
the case study.
Very good but not thorough
knowledge about calculus in
AI. The calculations and
codes are accurate. The
codes are occasionally not
efficient.
Excellent and thorough
understanding of calculus
in AI. The calculations and
codes are accurate and
completely e
or-free. The
most efficient
implementations are given.
Work demonstrates the
knowledge and
understanding of
probability in AI
20%
Little or no knowledge of
probability in AI.
The calculations and codes
are mostly inco
ect.
Acceptable but further work is
equired to show the
knowledge of probability in
AI. The calculations and codes
are mostly co
ect with
occasional inaccuracies and
e
ors.
Good level of knowledge
about probability in AI. The
calculations and codes are
co
ect with no e
ors. The
codes are not efficient for
the case study.
Very good but not thorough
knowledge about probability
in AI. The calculations and
codes are accurate. The
codes are occasionally not
efficient.
Excellent and thorough
understanding of
probability in AI. The
calculations and codes are
accurate and completely
e
or-free. The most
efficient implementations
are given.
Work demonstrates the
knowledge and
understanding of
statistics in AI
Little or no knowledge of
statistics in AI.
The calculations and codes
are mostly inco
ect.
Acceptable but further work is
equired to show the
knowledge of statistics in AI.
The calculations and codes
are mostly co
ect with
Good level of knowledge
about statistics in AI. The
calculations and codes are
co
ect with no e
ors. The
codes are not efficient for
Very good but not thorough
knowledge about statistics
in AI. The calculations and
codes are accurate. The
codes are occasionally not
Excellent and thorough
understanding of statistics
in AI. The calculations and
codes are accurate and
completely e
or-free. The
MFA501 Assessment 2 Brief Problem Sets Modules 6, XXXXXXXXXXPage 4 of 5
20% occasional inaccuracies and
e
ors.
the case study.
efficient.
most efficient
implementations are given
The reflective essay
demonstrates the
knowledge and
understanding of the
whole process of
implementing and using
the mathematical
models and methods to
solve the case study.
20%
The reflective essay
includes no or little
sections and concepts
equired. There is no or
little elaborations or
justifications on the use of
the mathematical models
and methods used to solve
the case study.
The reflective essay includes
some of the sections and
concepts required. There is
little elaborations or
justifications to demonstrate
the knowledge and
understanding of the whole
process of implementing and
using the mathematical
models and methods to solve
the case study.
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are not
discussed well to show the
knowledge and thorough
understanding of the whole
process of implementing
and using the mathematical
models and methods to
solve the case study.
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are not
thorough and in-depth to
demonstrate mastery of the
whole process of
implementing and using the
mathematical models and
methods to solve the case
study.
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are thorough
and show the mastery of
the whole process of
implementing and using the
mathematical models and
methods to solve the case
study.
The following Subject Learning Outcomes are addressed in this assessment
SLO a) Formulate key mathematical concepts used in Artificial Intelligence.
SLO b) Apply mathematical techniques in manipulating large data sets, and in designing and analysing experimental work in AI.
SLO c)
Use standard mathematical notations and terminologies in statistics, probabilities, linear alge
a, vectors, matrixes, differential calculus,
and logical reasoning.
SLO d) Compute accurately standard computations in statistics, probabilities, linear alge
a, vectors, matrixes and differential calculus.
MFA501 Assessment 2 Brief Problem Sets Modules 6, XXXXXXXXXXPage 5 of 5
Assessment 2 programming exercises:
Set 1 (to
e
submitte
d by
Module
6 (Week
6))
Choose two of these programming exercises:
- Write a program to find the Eigenvalues of a n-by-n matrix.
(bonus mark for the case that you can make it work for any
nϵN; if you can’t simplify it for n=2,3,4)
- Write a program to calculate Integral of a function.