Overview Projects Discussions Activities Evaluation & Reflection

Machine Learning Portfolio

Module Overview

This module explores the fundamental concepts and practical applications of machine learning in artificial intelligence. Through a combination of theoretical understanding and hands-on practice, we delve into various aspects of machine learning, including:

  • Supervised and unsupervised learning algorithms
  • Neural networks and deep learning
  • Model evaluation and validation
  • Feature engineering and selection
  • Real-world applications and case studies

Learning Outcomes

  1. Articulate the legal, social, ethical and professional issues faced by machine learning professionals
  2. Understand the applicability and challenges associated with different datasets for machine learning algorithms
  3. Apply and critically appraise machine learning techniques to real-world problems
  4. Develop effective team collaboration skills in a virtual professional environment

Formative & e-Portfolio Activities

Seminar Preparation EDA Tutorial Unit 2

A comprehensive tutorial on Exploratory Data Analysis using the Auto-mpg dataset, demonstrating key techniques in data visualization and statistical analysis.
Seminar Preparation Linear Regression with Scikit-Learn Unit 4

An in-depth analysis of the relationship between global population and GDP per capita using linear regression techniques, including correlation analysis and model evaluation.
e-Portfolio Component Jaccard Analysis Unit 5

An analysis of the Jaccard similarity index and its applications in machine learning.
Seminar Preparation K-Means Clustering Tutorial Unit 6

Analysis of K-Means clustering techniques applied to different datasets, including Iris, Wine, and WeatherAUS.
e-Portfolio Component Perceptron Analysis Unit 7

Exploration of neural networks through perceptron implementations.
Seminar Preparation Emerging Research in ANN Unit 8

Analysis of fraud detection in finance and ethical implications of AI in personal insurance.
e-Portfolio Component Gradient Descent Analysis Unit 8

Implementation and analysis of gradient descent algorithm, exploring the impact of learning rate and iterations on model convergence.
e-Portfolio Component CNN Analysis and Ethical Considerations Unit 9

Exploration of Convolutional Neural Networks for object recognition, including hands-on model testing and analysis of ethical implications in real-world applications.
e-Portfolio Component Model Performance Measurement Unit 11

Analysis of model performance metrics including AUC and R², exploring their impact on model evaluation and ethical considerations in real-world applications.
Seminar Preparation Future of Machine Learning Unit 12

Analysis of predictive machine learning models in Industry 4.0, exploring their impact on the manufacturing sector and ethical considerations.
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