Global Masters in Applied AI from Deakin University

- Introduction to Key Terminology (Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Large Language Model) - History and Evolution of AI - Business Problems and Solution Spaces Across Different Industries

- Introduction to Python - Environment Setup - Google Colab - Fundamental Python Programming Constructs - Variables, Data Types, Data Structures (List, Dictionary), Conditional Statements

- Week 1: Introduction to Python - Week 2: Data Manipulation - Week 3: Exploratory Data Analysis - Week 4: Project Week

- Week 5: Linear Regression - Week 6: Decision Trees - Week 7: K-means Clustering - Week 8: Project Week - Week 9: Learning Break

- Week 10: Bagging - Week 11: Boosting - Week 12: Model Tuning - Week 13: Project Week

- Week 14: Introduction to Neural Networks - Week 15: Optimizing Neural Networks - Week 16: Projects Week

- Week 17: Word Embeddings - Week 18: Attention Mechanism and Transformers - Week 19: Large Language Models and Prompt Engineering - Week 20: Retrieval Augmented Generation - Week 21: Project Week - Week 22: Learning Break

- Week 23: Introduction to AI Agent Workflows - Week 24: Planning and Reasoning in AI Agents - Week 25: Evaluating AI Agents - Week 26: Project Week

- Week 27: Introduction to Model Deployment - Week 28: Containerization - Week 29: Projects Week

- Code Generation Using GenAI - Image Creation Using GenAI - Speech Recognition Using GenAI

- Overview of Computer Vision - Image Processing - Convolutional Neural Networks

- Probability Fundamentals - Probability Distributions - Sampling and Central Limit Theorem - Estimation Theory - Hypothesis Testing

- Introduction to Recommendation Systems - Market Basket Analysis - Popularity-Based and Content-Based Recommendation Systems - Collaborative Filtering - Hybrid Recommendation Systems

- Introduction to DB and SQL - Fetching, Filtering, and Aggregating Data - Inbuilt and Window Functions - Joins and Subqueries

Reinforcement Learning (RL) Key Highlights: • Work with MDP variants such as discrete-time MDPs, Semi-MDPs (SMDP), continuous-time MDPs, POMDPs, and MOMDPs • Apply core RL techniques, including multi-armed bandits, value iteration, policy gradient, temporal difference learning, and reward design • Understand advanced concepts such as on-policy vs off-policy learning, eligibility traces, feature construction, and continuous action spaces • Explore emerging areas, including deep RL, multi-agent systems, transfer learning, hierarchical and curiosity-driven learning Engineering AI Solutions Key Highlights: • Understand the process and key characteristics of developing AI solutions and how they differ from traditional software development • Design, develop, deploy, and maintain AI solutions using modern tools, frameworks, and libraries • Apply engineering principles and the scientific method with appropriate rigour in experimentation • Manage stakeholder expectations and guide the operationalisation of AI solutions from inception to deployment and ongoing maintenance

Robotics, Computer Vision and Speech Processing Key Highlights: • Understand how computer vision and speech processing enable sensing and interaction in robotics • Analyse existing algorithms and their applications in real-world robotic scenarios • Investigate state-of-the-art machine learning techniques used in vision and speech domains • Develop novel solutions integrating computer vision and speech processing for robotics applications Deep Learning Key Highlights: • Understand core deep learning theories, including computational graphs and representation learning • Build deep learning models for structured and unstructured data • Learn key techniques such as convolutional neural networks, recurrent networks, and neural embedding methods • Explore real-world applications of deep learning widely adopted across industries