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AI Skillset Course Summary

Course Title: ICTSS00120 - Artificial Intelligence Skill Set

Delivery Period: 2024, S2

Location: Perth

Lecturer: Jordan Hill

Overview

The AI Skillset course is designed to provide a comprehensive understanding of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Spanning 20 weeks, this course equips students with the skills to train and use their own AI models. The course emphasizes practical applications, ethical considerations, and the continuous evolution of AI technologies.

Objectives

  • Gain foundational understanding of AI, ML, and DL.
  • Develop the ability to identify opportunities for AI application in various fields.
  • Train and evaluate machine learning models.
  • Understand and apply ethical principles in AI development and deployment.

Key Topics

  1. Introduction to AI, ML, and DL: Historical overview, key features, and applications.
  2. Data for Machine Learning: Types of data, data sources, preprocessing techniques.
  3. Essentials of ML and DL Technologies: Supervised/Unsupervised learning, deep learning basics.
  4. Data Bias and Ethics in AI: Recognizing biases, ethical frameworks.
  5. Task Automation: Identifying and evaluating AI opportunities.
  6. Deep Learning Foundations: Neural networks, hidden layers, training methods.
  7. Transformers and Embeddings: Latest advancements and practical applications in NLP and computer vision.
  8. Hyperparameter Tuning: Techniques to optimize model performance.
  9. Practical Project Work: Real-world application of AI to automate tasks.

Assessments

  1. Identify Opportunities for AI Task Automation (Week 1-7):

    • Task: Research and pitch AI solutions for automation.
    • Due Date: Week 7

  2. Knowledge-Based Assessment 1 (Weeks 1-6):

    • Task: Demonstrate initial knowledge of AI, ML, and DL concepts.
    • Due Date: Week 10

  3. Knowledge-Based Assessment 2 (Weeks 8-13):

    • Task: Demonstrate technical knowledge of deep learning processes.
    • Due Date: Week 15

  4. Final Project: Apply Machine Learning to Task Automation (Weeks 8-18):

    • Task: Use ML to automate a work task; includes data sourcing, model design, training, and evaluation.
    • Due Date: Week 18

Learning Materials

  • Books and Reading: Essential and extension readings from AI literature and classic science fiction exploring AI themes.
  • Online Resources: Links to academic papers, tutorials, and hands-on activities via platforms like Kaggle and Huggingface.
  • In-Class Activities: Research, discussions, and lab-based practicals aligning with weekly themes.

Prerequisites

  • Proficiency in Python programming.
  • Understanding of basic computational problem-solving techniques.
  • Ability to work independently on computational tasks with provided resources.

Additional Requirements

  • Student Supplies: USB (16GB+), home workstation, personal notes, Kaggle/Huggingface/Google accounts.
  • College Supplies: On-campus workstations, access to academic journals, GPU accelerated compute environments.

Key Learning Outcomes

  • A deep understanding of AI technologies and their real-world applications.
  • Skills to train, evaluate, and optimize machine learning models.
  • Knowledge to ethically assess and deploy AI systems.
  • Preparation for continuous learning and adaptation in the ever-evolving field of AI.

Conclusion

The AI Skillset course is a robust program aimed at developing practical skills and deep theoretical understanding necessary for proficiently applying AI technologies across various domains. It is structured to provide both foundational knowledge and hands-on experience, preparing students for a dynamic career in AI.