Mine Automation and Data Analytics

Week 1: Lecture 1: Introduction to Automation, Principles and Strategies of Automation and enhancement of Productivity Lecture 2: Essential Elements of an Automated SystemWeek 2: Lecture 3: Autonomous Mining System I: Autonomous Haulage Systems Lecture 4 :Autonomous Mining System II: Automated Drilling System Lecture 5: Autonomous Mining System III: Fleet Management System: TDSWeek 3: Lecture 6: Computerised Maintenance Management System, ERP for Mining Industry Lecture 7 :Mine Robotics: Mining Remote Operations & ControlWeek 4: Lecture 8 Proximity Sensors and Control Systems Lecture 9 Radar Systems, RFID in Mining Engineering Lecture 10 Geo-fencing, CCD camera in Mining for safety and managementWeek 5: Lecture 11 Global Navigational Satellite System in Mining production planning and efficient control of the machine Lecture 12 Automated Communication and Tracking Technologies: Image ProcessingWeek 6: Lecture 13Automated Communication and Tracking Technologies: SCADA Lecture 14 Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations Part 1 Lecture 15 Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations Part 2Week 7: Lecture 16 Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations Part 3 Lecture 17 Descriptive Statistics: IntroductionWeek 8: Lecture 18 Probability Distributions and Inferential Statistics: Hypothesis tests Part 1 Lecture 19 Probability Distributions and Inferential Statistics: Hypothesis tests Part 2 Lecture 20 Probability Distributions and Inferential Statistics: Hypothesis tests Part 3Week 9: Lecture 21 Probability Distributions and Inferential Statistics: Hypothesis tests Part 4 Lecture 22 Regression & ANOVAWeek 10: Lecture 23 Machine Learning: Introduction Lecture 24 Perceptron: Linear Classifier Lecture 25 Support Vector MachineWeek 11: Lecture 26 Concepts of Supervised Learning: Neural Networks, Deep learning Lecture 27 Unsupervised Learning and Challenges for Big Data Analytics: ClusteringWeek 12: Lecture 28 Application of Big Data Analytics and Artificial Intelligence (AI) in Mining Lecture 29 Case studies on Cognitive Maintenance of Mining Systems Lecture 30 Case studies on Orebody modelling and Mine Design etc.