Module #1 Introduction to IoT and Machine Learning Overview of IoT systems, machine learning concepts, and their intersection
Module #2 IoT Data Characteristics and Preprocessing Understanding IoT data, handling missing values, and data normalization
Module #3 Introduction to Supervised Learning Basics of supervised learning, regression, and classification
Module #4 Linear Regression for IoT Applications Applying linear regression to IoT data, including sensor calibration and prediction
Module #5 Classification Algorithms for IoT Introduction to classification algorithms, including logistic regression, decision trees, and random forests
Module #6 Unsupervised Learning for IoT Clustering Clustering IoT data using k-means, hierarchical clustering, and density-based clustering
Module #7 Dimensionality Reduction for IoT Data Techniques for reducing IoT data dimensionality, including PCA, t-SNE, and autoencoders
Module #8 Anomaly Detection for IoT Systems Detecting anomalies in IoT data using statistical methods, machine learning, and deep learning
Module #9 Introduction to Deep Learning for IoT Basics of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks
Module #10 Convolutional Neural Networks for IoT Image Processing Applying CNNs to IoT image processing, including object detection and classification
Module #11 Recurrent Neural Networks for IoT Time Series Analysis Applying RNNs and LSTMs to IoT time series data, including forecasting and anomaly detection
Module #12 Edge AI and Distributed Machine Learning for IoT Deploying machine learning models at the edge, including distributed learning and federated learning
Module #13 Deep Reinforcement Learning for IoT Control Systems Applying deep reinforcement learning to IoT control systems, including Markov decision processes and Q-learning
Module #14 IoT Data Management and Storage Managing and storing IoT data, including data lakes, NoSQL databases, and time-series databases
Module #15 Real-time IoT Data Processing and Analytics Processing and analyzing IoT data in real-time, including stream processing and event-driven architecture
Module #16 IoT Security and Privacy for Machine Learning Securing IoT systems and protecting user privacy in machine learning applications
Module #17 Human-Machine Interface for IoT Systems Designing user interfaces for IoT systems, including data visualization and voice assistants
Module #18 Case Studies in Intelligent IoT Systems Real-world examples of intelligent IoT systems, including smart homes, industrial automation, and healthcare
Module #19 Edge Computing and IoT Gateway Development Developing IoT gateways and edge computing systems, including hardware and software design
Module #20 Machine Learning Model Deployment and Monitoring Deploying and monitoring machine learning models in IoT systems, including model serving and explainability
Module #21 IoT System Design and Architecture Designing and architecting IoT systems, including system integration and testing
Module #22 Standards and Interoperability for IoT Understanding IoT standards and interoperability, including protocols and data formats
Module #23 IoT and Artificial Intelligence Ethics Ethical considerations in IoT and AI development, including fairness, transparency, and accountability
Module #24 Specialized IoT Applications Exploring specialized IoT applications, including autonomous vehicles, smart cities, and agriculture
Module #25 IoT Research and Future Directions Research trends and future directions in IoT and machine learning
Module #26 Group Project:Developing an Intelligent IoT System Students will work in groups to develop and present an intelligent IoT system project
Module #27 Individual Project:IoT Data Analysis and Machine Learning Students will work on an individual project to analyze IoT data and develop a machine learning model
Module #28 Review and Practice Review of key concepts and practice exercises
Module #29 Final Project Presentations Students will present their final projects
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Intelligent IoT Systems career
