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ACS61013 Assignment

Creation date：2024-05-16 17:28:45

Belonging category：计算机computer science

Tag ACS61013

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Module title: ACS61013

Assignment Name: Coursework 1

Tools to use: Majority of the MATLAB code you need to complete the assignment are
available from various lab sessions. If you are comfortable using Python, you are free to use
it. You are also free to use Orange for various aspects of the coursework as required.
Tasks and Mark Scheme: The aim of this coursework is to design, implement and evaluate
effective machine-learning pipelines for various tasks. The specific tasks and the
corresponding marking schemes are given in the table below. It is up to you to decide how
you approach the various tasks, design a solution and write-up your results. For each task,
the mark within the grade boundary will be based on your discussion in your report and results
obtained.
Task/Assessment Description Mark
Range
Level of
achievement
Task 1: Conduct and write a domain analysis that discusses
the important weather features that affect the energy usage of
a house as well as discuss the weather features that could
affect the energy generated by the solar panel attached to the
house.

Discuss how what you have found from your domain analysis
will support and be carried over to other parts of your work.
0-10% 1
Task 2: Achieve level 1 as well as conduct data cleaning,
pre-processing and feature engineering.

Discuss how you used your understanding of the domain from
level 1 to support this task. This should also involve
discussions on deciding which features to drop and which
relevant features to keep. Support your explanation by
applying dimension reduction (e.g PCA or Hierarchical
Clustering Analysis) techniques.
10-20% 2
Task 3. Achieve all the previous levels as well as build a
regression model (decide which hypothesis function that is
best to use e.g polynomial or linear etc) or neural network
model to predict the value of energy usage from at least two
weather features you deemed important to keep from Task 2.
20-30% 3
Task 4. Achieve all the previous levels as well as use
learning curves to discuss how effective your regression
model machine learning pipeline is at preventing overfitting
and underfitting.
30-45% 4
Task 5. Achieve all the previous levels plus discuss which
cross validation technique you applied in Task 4 above and
why.
45-50% 5
Task 6. Using the features you consider most important to
this challenge, apply a classification machine learning
methodology (e.g Decision Trees or Neural Network) to build
a model that predicts when energy usage of a house will be
LOW, MEDIUM or HIGH.

Use the classification metrics of confusion matrix, accuracy,
precision and recall to explain your results.

Can your model explain or highlight which appliance is used
most when the energy usage is LOW, MEDIUM or HIGH?
50-65% 6
Task 7. From Task 6, compare the results of the Decision
Tree methodology with Neural Network methodology using
the classification metrics of confusion matrix, accuracy,
precision and recall to explain your results.

Demonstrate and explain how model complexities (both
Decision Tree and Neural Network) affect the results you
obtain.
65-80% 7
Task 8. Achieve all the previous levels and the below:
 Using the dataset given, compare the results of the
machine learning algorithms above with the results of
two other algorithms that we have not covered in class.
 Discuss the mathematical peculiarities of the
algorithms you have chosen (strengths and
weaknesses) and how they impact the results you
obtain.
 Apply the appropriate metrics to compare the
algorithms you have chosen with the ones we have
used in class.
80-100% 8

Technical Report and code
Write your results in no more than a 15 page technical report. Make sure your report has
a table of content, sections, discussion and conclusions.
You must create a MATLAB (or Python code) and an Orange pipeline design for your
solution(s). Support your report with an Orange pipeline design and MATLAB code. Make
sure you provide comments in your MATLAB code as well as instructions on how to run it.
Hand in your report (.pdf), software (Orange and MATLAB (or Python)) via Blackboard by
11pm on the 9th of December 2022. This course work makes up 60% of your total module
mark.
Appendix
Most of the features in the dataset are self-explanatory. However, I have highlighted a few
below:
Features Description
Time The time is provided in the current epoch unix timestamp format.
See this link
Use [kW] This is the energy usage of the house. This is similar to the house
overall [kW] column.
Gen [kW] The is the energy generated by the solar panel attached to the
building. This feature is similar to the Solar [kW] column.
Weather Icon These are weather icons used to indicate weather conditions.
PrecipIntensity This means precipitation intensity
PrecipProbability This means precipitation probability

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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