DSC7053 Artificial Intelligence And Machine Learning, Assignment, VUC, Malaysia

INTRODUCTION TO THE UNIT

The course aims at Artificial Intelligence (AI) and Machine Learning, explore use cases and applications of AI, and understand AI and machine learning concepts and terms like neural networks. You will have an understanding of AI and architecture powered applications based on current business industry requirements. The subject will also enable you to understand smart business world related to products and solutions in order to support fact-based decision making in Business world.

Course Learning Outcomes (CLO)

1. Evaluate business growth opportunities within the business environments using artificial intelligence and machine learning concepts. (C5, PLO1)
2. Propose AI and ML applications to improve the business organization’s operations. (C6, PLO2)
3. Adapt specialized professional business digital transformation skills to enhance business work tasks efficiency. (A5, PLO6)
4. Support self-improvement skills while using artificial intelligence and machine learning concepts to improve business service efficiency. (A5, PLO8)

Assessment 1: Coursera Online Quiz (20%)

Title : Introduction to Artificial Intelligence (AI)
Institution : IBM

About this Course :

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

TOPICS

1 What is AI? Applications and Examples of AI
2 AI Concepts, Terminology, and Application Areas
3 AI Issues, Ethics and Bias
4 Final Assignment Part One

Assessment 2: Individual Written Assignment 1 (30%)

Exploring the Role of Artificial Intelligence in Business Transformation

Disruptive innovation is a powerful driver of change in business environments. Artificial intelligence (AI) has emerged as a transformative technology with the potential to disrupt entire industries and redefine competitive landscapes. This assignment explores the role of AI in fostering disruptive innovation and achieving a competitive advantage.

1. Define disruptive innovation and explain how it differs from sustaining innovation. Provide examples of disruptive innovations in various industries.

2. Discuss the key characteristics of AI technologies that make them potential drivers of disruptive innovation. How do AI capabilities such as machine learning, natural language processing, and computer vision contribute to disruptive innovation?

3. Analyse how AI is being used to disrupt the healthcare industry. Discuss specific examples of AI applications and their potential impact on patient care, healthcare delivery, and business models.

4. Evaluate the potential of AI to disrupt the financial services industry. Examine how AI can be used to improve customer experience, enhance risk management, and develop new financial products.

5. Discuss the ethical implications of AI-driven disruption, including concerns about job displacement, bias, and transparency. How can organisations mitigate these risks and ensure responsible AI development?

6. Analyse the potential challenges and opportunities associated with integrating AI into existing business processes within the selected industry.

Assessment 3: Individual Written Assignment 2 (50%)

Application of Machine Learning

This assignment is designed to test your understanding on machine learning and hands-on experience with machine learning techniques for a given dataset. You will have the opportunity to apply various machine learning techniques to the datasets. This will demonstrate how predictive analytics can support data-driven decision making in areas like marketing, risk analysis, and operations.

Part A

This section tests your understanding of various aspects of machine learning.

1. Explain briefly the difference between classification and regression tasks in machine learning. Provide examples of each type of task and discuss the metrics used to evaluate their performance.

2. Discuss the challenges of handling imbalanced datasets in machine learning. Describe techniques used to address class imbalance and improve the performance of models trained on such datasets.

Part B

Utilise any dataset containing information about various housing characteristics (such as square footage, number of bedrooms, location, etc.) and their corresponding prices. Employ machine learning techniques to analyse the dataset and understand the relationship between housing characteristics and prices.

Instructions:
1. Load the dataset into a Jupyter Notebook using Python.

2. Perform exploratory data analysis (EDA) to gain insights into the dataset.

3. Preprocess the data, including handling missing values, encoding categorical variables, and scaling numerical features.

4. Split the dataset into training and testing sets.

5. Train a regression model to predict housing prices based on the characteristics.

6. Evaluate the performance of the trained model using appropriate evaluation metrics (e.g., mean squared error, R-squared).

7. Analyse the results and interpret the model’s coefficients to understand the impact of different housing characteristics on prices.

8. Visualise the relationships between selected features and prices using appropriate plots (e.g., scatter plots, regression plots).

Submit the following:

1. Your Jupyter Notebook (.ipynb) containing the Python code. Ensure that your notebook is well-documented, including comments explaining each step of the analysis and reasoning behind the choices made
2. Analyse and interpret the results for step 6, 7 and 8 above.
3. Summarise the key insights and findings from your analysis in a 250-word

Answer

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