Fundamentals of Machine Learning
Participants delve into the fundamental principles of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and deep learning. They learn about different machine learning algorithms, optimization techniques, and evaluation metrics.
MLM 112 – Data Pre-processing and Feature Engineering
Data Pre-processing and Feature Engineering
focuses on data pre-processing techniques and feature engineering methods specific to mining applications. Participants learn how to clean, transform, and pre-process raw mining data to prepare it for machine learning algorithms. They also explore methods for extracting informative features from mining datasets.
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MLM 113 – Predictive Modelling in Mining
Predictive Modelling in Mining
Participants study advanced predictive modelling techniques used in mining applications, such as regression analysis, classification algorithms, time series forecasting, and anomaly detection. They learn how to build predictive models to optimize mining processes, improve resource estimation, and predict equipment failures.
MLM 114 – Optimization and Control with Machine Learning
Optimization and Control with Machine Learning
Participants explore how machine learning techniques can be applied to optimize and control various aspects of mining operations, including production scheduling, equipment optimization, process control, and energy management. They learn about optimization algorithms, reinforcement learning approaches, and control theory principles.
MLM 115 – Remote Sensing and Image Analysis
Remote Sensing and Image Analysis
Machine learning plays a crucial role in analysing remote sensing data and images acquired from satellites, drones, or other aerial platforms in mining applications. Participants learn about image processing techniques, object detection algorithms, and classification methods for extracting useful information from remote sensing data.
MLM 116 – Geological Data Analysis
Geological Data Analysis
Participants gain expertise in analysing geological data using machine learning techniques. They learn how to integrate geological data with other types of mining data, such as drill hole data, geochemical data, and geophysical data, to gain insights into mineral exploration, resource characterization, and geological mapping.
MLM 117 – Big Data Analytics and Cloud Computing
Big Data Analytics and Cloud Computing
Participants explore techniques for handling large-scale mining datasets, including distributed computing, parallel processing, and cloud-based data analytics platforms. They learn how to leverage big data technologies to analyse massive volumes of mining data efficiently.
MLM 118 – Case Studies and Practical Applications:
Case Studies and Practical Applications
The program may include case studies, projects, and hands-on exercises that apply machine learning techniques to real-world mining datasets and problems. Participants gain practical experience in data analysis, model development, and interpretation of results in mining contexts.
MLM 119 – Ethical and Regulatory Considerations
Ethical and Regulatory Considerations
Participants learn about ethical considerations and regulatory requirements related to the use of machine learning in mining applications. They explore issues such as data privacy, algorithmic bias, and transparency in machine learning models.
