代写DATA4800 Artificial Intelligence and Machine Learning代写C/C++编程

DATA4800 Subject Outline

Subject Code:	DATA4800
Subject Name:	Artificial Intelligence and Machine Learning
Credit Points:	Four
Pre-requisite:	STAM4000
Co-requisite:	DATA4100
Subject Coordinator(s):	Please see MyKBS subject page for details
Workshop Facilitator(s):	Please see MyKBS subject page for details
Study Mode:	On campus and online
Required Materials:	Please see MyKBS subject page for resources

Subject Description

This subject builds upon previous analytics topics and explores advances in algorithmic Artificial

Intelligence (AI) techniques such as Machine Learning (ML). Students will first explore both

supervised and unsupervised ML, which include basic classification methods such as Decision Trees, Random Forests, Logistic Regression and Support Vector Machines and also basic

dimensionality reduction and clustering algorithms, such as PCA and K-means clustering.

Students will then investigate the theory and application of neural nets and deep learning in AI. The subject also introduces contemporary methods in ML such as convolutional neural

networks for image analytics as well as transformer models for natural language processing.

Subject Website

Students will need access to technology resources (including the internet) to undertake this     subject. The subject has a dedicated website called MyKBS. It is important that students visit   the MyKBS site regularly for subject updates and general information. The MyKBS site can be accessed at:https://elearning.kbs.edu.au/. All assessments are electronically lodged via

MyKBS.

Subject Learning Outcomes

Upon successful completion of this subject, students should be able to demonstrate achievement of the following learning outcomes.

LO1:	Evaluate the advantages and disadvantages of artificial intelligence in business
LO2:	Create and communicate business insights through machine learning
LO3:	Explore relational and non-relational database tools, and how they support machine learning
LO4:	Analyse chatbots and applications of natural language processing in artificial intelligence
LO5:	Investigate the benefits to business and individuals of smart technologies

Kaplan Graduate Attributes

Kaplan has a series of Graduate Attributes that define the philosophy underpinning its subjects and programs. Further details about the Graduate Attributes can be accessed at:

https://www.kbs.edu.au/about-us/school-policies.

Recommended Resources

The recommended (but optional) textbook for this subject is “Machine Learning in Business” 2e  (John Hull, 2020) or 3e (John Hull, 2022). Further, students are expected to access and utilise a wide range of information sources that are provided on MyKBS under weekly workshop content  tabs. The Kaplan Library also has an extensive collection of resources that are available to support student research and study needs. The library can be accessed at

https://library.kaplan.edu.auor via MyKBS.

Software

The required software for this subject is:

Orange Data Mining. All analysis in this course will be carried out using the Orange Data Mining software exclusively:

1.   Download at:https://orangedatamining.com/(you do not have to do this before Week 1, but you are encouraged to do so).

 

Google Docs and Sheets. All word processing and report writing in this course will be carried out using Google Docs and Sheets.

1.   Go to:https://accounts.google.com/and “create account” using the same email address  that is listed on your MyKBS profile (you do not need to do this if your email address is a Gmail address).

Assessments Overview

This is an assessed and graded subject. A student’s overall grade will be calculated from the

marks for each assessment task based on the relative assessment weightings shown in the table below. Students must obtain an overall mark for the subject of at least 50% to be able to pass the subject. Details about the subject assessments can be accessed at the subject’s website at

MyKBS. Further details about Kaplan’s assessment policy, including information about late submissions, can be accessed athttps://www.kbs.edu.au/about-us/school-policies.

Assessment	Weight	Learning   outcomes	Mode of submission	Submissio n week	Details
Assessment 1 Group prediction project – classification.	30%	LO1, LO2	Group: In Class     Individual: Via Turnitin	Week 5	Teamwork: Building & analysing decision tree and logistic regression models for a    simple dataset
Assessment 2 Individual implementation and problem solving (puzzle)	30%	LO3, LO5	Individual: In class implementatio n and problem solving   Participation: During class	Week 10	Implementing and analysing neural networks and other predictive models
Assessment 3 Individual project.	40%	LO2, LO3, LO4, L05	Individual: Via Turnitin   Video Quiz	Week 13	Integration of knowledge using a business problem

Student Feedback

Every trimester, each subject is reviewed as part of a continuous process of quality assurance.

Students are surveyed as part of this review process. This is known as the SELTS survey, that is: Student Experience, Learning and Teaching Survey. The survey takes place in the later part of

the trimester and participation is voluntary, confidential, and highly encouraged. The results of the survey are factored into continual workshop delivery and subject development by the subject

facilitators and coordinators. Results are only made available to the subject facilitators after the release of the final grades in the subject. Should students have suggestions for improvement about their subject during the trimester, discussion with their workshop facilitator is always encouraged.

Weekly Workshop Schedule

Weekly on-campus and online workshops are designed to offer interesting and effective activities

to help you learn best. These may include peer-to-peer activities, videos, online discovery, real- life content examples, open discussion, assessment guidance discussions, and feedback opportunities.

	Topic
Week 1	Introduction to Machine Learning and Ethics
Week 2	Supervised Learning: Linear and Logistic Regression, KNN
Week 3	Unsupervised Learning: K Means and PCA + Orange Lab
Week 4	Supervised Learning: Decision Trees and Random Forests
Week 5	In-class assessment 1
Week 6	Introduction to Neural Networks
Week 7	Image Classification with Deep CNNs + Orange Lab
Week 8	Introduction to Natural Language Processing
Week 9	Natural Language Processing with BERT + Orange Lab
Week 10	In-class assessment 2
Week 11	Reinforcement Learning and Autonomous Agents
Week 12	Model Interpretability and Subject Summary

Academic Integrity Policy

KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.

.    What is academic integrity and misconduct?

.    What are the penalties for academic misconduct?

.    What are the late penalties?

.     How can I appeal my grade?

The answers to these questions can be accessed athttps://www.kbs.edu.au/about-us/school- policies.

Length Limits for Assessments

Penalties may be applied for assessment submissions that exceed prescribed limits.

Study Assistance

Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on theMyKBS Academic Success Centrepage. Further details can be accessed at  https://elearning.kbs.edu.au/course/view.php?id=1481