Lecture 1 - Introduction to Automation
Lecture 2 - Principle of Automation and Strategies
Lecture 3 - Elements of Automated System
Lecture 4 - Elements of Automated System (Continued...)
Lecture 5 - Autonomous Haulage System
Lecture 6 - Autonomous Haulage System (Continued...)
Lecture 7 - Automated Drilling System
Lecture 8 - Automated Drilling System (Continued...)
Lecture 9 - Fleet Management System
Lecture 10 - Fleet Management System (Continued...)
Lecture 11 - Introduction to CMMS
Lecture 12 - Enterprise resource planning (ERP) system
Lecture 13 - Remote operation and control center
Lecture 14 - Remote operation and control center
Lecture 15 - Proximity Sensors
Lecture 16 - Proximity Sensors and Control System
Lecture 17 - Sensing System: Radar Technology
Lecture 18 - RFID in Mining Engineering
Lecture 19 - Introduction to Geo-fencing
Lecture 20 - CCD camera in Mine safety and management
Lecture 21 - GNSS in Mining
Lecture 22 - GNSS Case Studies - Part I
Lecture 23 - GNSS Case Studies - Part II
Lecture 24 - Image Processing and Analysis in Remote Sensing
Lecture 25 - Basics of Digital Image Processing
Lecture 26 - Automated communication andtracking technologies: Image processing
Lecture 27 - Automated Communication and Tracking Technologies: SCADA
Lecture 28 - SCADA and its Application in Mining
Lecture 29 - Introduction to VR Systems
Lecture 30 - Virtual Reality Application in Mining
Lecture 31 - Introduction to Augmented Reality (AR)
Lecture 32 - Augmented Reality Application in Mining
Lecture 33 - Introduction - I
Lecture 34 - Introduction - II
Lecture 35 - Introduction to Probability and its associated terms
Lecture 36 - Introduction to Probability and its associated terms
Lecture 37 - Discrete Random Variable - Part I
Lecture 38 - Discrete Random Variable - Part II
Lecture 39 - Continuous Random Variable - Part I
Lecture 40 - Continuous Random Variable - Part II
Lecture 41 - Hypothesis Testing - I
Lecture 42 - Hypothesis Testing - II
Lecture 43 - t-test
Lecture 44 - Chi-Squared Test
Lecture 45 - Introduction to Machine Learning
Lecture 46 - Regression
Lecture 47 - Logistic Regression
Lecture 48 - K Nearest Neighbor
Lecture 49 - Support Vector Machine
Lecture 50 - Naïve Bayes Classifier
Lecture 51 - Artificial Neural Networks
Lecture 52 - K Means Clustering
Lecture 53 - DBSCAN
Lecture 54 - Principal Component Analysis (PCA)
Lecture 55 - Application of Big Data Analytics in Mining
Lecture 56 - Big Data and AI Used Cases
Lecture 57 - Cognitive Maintenance in Mining
Lecture 58 - Cognitive Maintenance Case Studies
Lecture 59 - Introduction to Orebody Modelling and Mine Design
Lecture 60 - Case studies on Orebody Modeling and Mine Design
