Machine Learning for Big Data
Description
Application of machine learning algorithms and techniques to big data problems including supervised and unsupervised learning, deep learning, and distributed machine learning frameworks.
Learning Objectives
Students will implement machine learning algorithms for big data applications, understand distributed machine learning concepts, apply supervised and unsupervised learning techniques to large datasets, develop deep learning models for big data problems, use machine learning libraries and frameworks like MLlib and TensorFlow, and evaluate model performance and scalability in big data environments.
Topics (10)
Application of supervised machine learning techniques to big data problems with focus on scalability, accuracy, and interpretability of models.
Pattern discovery and data exploration using unsupervised machine learning methods adapted for big data environments and high-dimensional datasets.
Advanced neural network architectures and deep learning techniques specifically applied to big data problems requiring complex pattern recognition and feature extraction.
Scalable machine learning using Apache Spark's MLlib library for distributed training and inference on large datasets across computing clusters.
Large-scale text processing and analysis using natural language processing techniques adapted for big data environments and multilingual datasets.
Advanced time series analysis using machine learning for forecasting, trend analysis, and anomaly detection in large-scale temporal datasets.
Comprehensive model assessment and validation methodologies ensuring reliability and generalizability of machine learning models in big data applications.
Operationalization of machine learning models in production including deployment strategies, monitoring, maintenance, and continuous integration for big data systems.
Advanced techniques for preparing and transforming large-scale datasets for machine learning including dimensionality reduction and automated feature engineering.
Large-scale image and video processing using computer vision techniques and deep learning for applications in surveillance, healthcare, and autonomous systems.