Applied Artificial Intelligence and Machine Learning

This course provides an knowledge to machine learning concepts, algorithms, and applications. Students will learn about supervised and unsupervised learning, model evaluation, and practical implementations.

Instructor: Dr. Viet Vo

Term: Term 2

Location: Adelaide City Campus East

Time: Mondays and Wednesdays, 11:00 AM - 13:00 PM

Course Overview

This is course is designed to delve into the intricacies of deep learning, focusing on cutting-edge applications and architectures. The course emphasises the practical application of deep learning techniques, particularly in handling image data using convolutional neural networks (CNNs). Students will explore a variety of deep learning frameworks and gain hands-on experience through detailed case studies that showcase the implementation of these technologies in real-world scenarios. From recognising visual patterns to enhancing image processing capabilities, the course offers a thorough examination of how deep learning can be leveraged to solve complex problems in various domains, preparing students for innovative challenges in the field of AI and ML.

  • Deep Learning Foundation
  • Neural Network
  • Advanced Cnn Architectures
  • Transformers And Their Applications

Prerequisites

  • Must have completed 1 of ARTI5002 Applied Artificial Intelligence and Machine Learning/ARTI6003 Machine Learning Algorithms

Textbooks

  • Primary: “Machine Learning: A Probabilistic Perspective” by Kevin Murphy
  • Reference: “Pattern Recognition and Machine Learning” by Christopher Bishop

Grading

  • Assignments: 75%
  • Open book quizes: 10%
  • Close book quizes: 15%

Schedule

Week Date Topic Materials
1 Sept 5 Course Introduction

Overview of machine learning, course structure, and expectations.
2 Sept 12 Linear Regression

Introduction to linear regression, gradient descent, and model evaluation.
3 Sept 19 Classification

Logistic regression, decision boundaries, and multi-class classification.
4 Sept 26 Decision Trees and Random Forests

Tree-based methods, ensemble learning, and feature importance.
5 Oct 3 Support Vector Machines

Margin maximization, kernel methods, and support vectors.
6 Oct 10 Midterm Exam

Covers weeks 1-5.
7 Oct 17 Neural Networks Fundamentals

Perceptrons, multilayer networks, and backpropagation.
8 Oct 24 Deep Learning

Convolutional neural networks, recurrent neural networks, and applications.