Recommended Prerequisites:
- Basic understanding of mining industry operations and terminology
- Familiarity with fundamental concepts of data analytics and statistics
- No prior coding experience required (coding templates provided)
- Prior exposure to GIS, geospatial data, or industrial automation is a plus but not mandatory
- Recommended: Prior exposure to GIS, geospatial data, or industrial automation is a plus but not mandatory
Course Outline:
Lesson 1: Introduction to AI in Mining
- 1.1 Overview of AI, ML & Deep Learning in Mining
- 1.2 Use Cases
- 1.3 Activity
Lesson 2: Machine Learning & Deep Learning for Mining
- 2.1 Introduction to ML & Deep Learning
- 2.2 Use Cases
- 2.3 Case Study
- 2.4 Hands-On Exercise
- 2.5 Activity
Lesson 3: AI in Mineral Exploration & Resource Modeling
- 3.1 AI for Smart Exploration & Orebody Modeling
- 3.2 Use-Cases
- 3.3 Case Study
- 3.4 Hands-on Exercises
- 3.5 Activity
Lesson 4: AI for Equipment Automation & Fleet Optimization
- 4.1 AI in Autonomous Vehicles & Robotics
- 4.2 Use Cases
- 4.3 Case Study
- 4.4 Hands-On Exercise
- 4.5 Activity
Lesson 5: AI in Predictive Maintenance & Asset Management
- 5.1 AI in Equipment Health Monitoring
- 5.2 Use Cases
- 5.3 Case Study
- 5.4 Hands-On Exercise
- 5.5 Activity
Lesson 6: AI for Environmental Compliance & Sustainability
- 6.1 AI-Powered Environmental Monitoring
- 6.2 Use Cases
- 6.3 Case Study
- 6.4 Hands-On Exercises
- 6.5 Activity: Group Exercise
Lesson 7: AI for Workforce Transformation & Ethical AI
- 7.1 Ethical AI, Workforce Augmentation & AI Regulations
- 7.2 Use Cases
- 7.3 Case Study
- 7.4 Hands-On Exercises
Lesson 8: AI in Mining Strategy & Implementation
- 8.1 AI-Driven Decision-Making in Mining
- 8.2 Use Cases
- 8.3 Case Study
