1. Introduction to Machine Learning for Civil Engineering<br>What is Machine Learning (ML), how it can be used to solve General Purpose tasks, Optimization System Design, use of ML for different Civil Engineering Areas<br>2. Basic Machine Learning Models for data pre-processing<br>Data sources in Civil Engineering Applications, including images, on-field data, drone data, IS codes, and audio datasets. Introduction to ML based pre-processing models like ARIMA, Wavelet, Fourier, etc. to filter these signals, Use of filtered signals for solving real-time Civil Engineering tasks<br>3. Use of ML models for data representation<br>What is Data Representation w.r.t. Civil Engineering, different ML methods for representing data that can be used for classification & post-processing applications.<br>4. Introduction to classification models for Civil Engineering Applications<br>What is classification, and how it can be used to optimize Civil Engineering Applications, use cases for Geotechnical Engineering, Structural Engineering, Water Resources Engineering, Environmental, and Remote sensing GIS applications<br>5. Classification Models for practical deployment in different Civil Engineering Applications<br>Introduction to kNN, Random Forests, Naïve Bayes, Logistic Regression, Multiple Layered Perceptron, and Fuzzy Logic models for classification, as applied to real time applications<br>6. Advanced Classification Models for different Civil Engineering Applications<br>Introduction to Convolutional Neural Networks (CNNs), advantages of CNNs over traditional methods, issues with CNNs when applied to Civil Engineering tasks, applications of CNNs for different fields of Civil Engineering<br>7. Advanced Classification Models II: Extensions to CNNs<br>Introduction to Recurrent Neural Networks (RNNs), Long-Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and their real-time applications to Civil Engineering tasks, sample GIS application and its solutions with different deep learning models<br>8. Bioinspired Computing models for Civil Engineering<br>Introduction to bioinspired computing, role of optimization in Civil Engineering, different bioinspired models, and their applications to solving traffic issues<br>9. Reinforcement Learning Methods & role of IoT in Civil Engineering Applications<br>What is reinforcement learning, introduction to IoT for Civil Engineering, use of reinforcement learning for low-power IoT-based Civil Engineering Applications<br>10. Solution to real time Civil Engineering tasks via ML<br>Case Study 1: Use of drones for construction monitoring, and their management via ML<br>Case Study 2: Conservation of water resources via bioinspired optimizations<br>Case Study 3: Reduction of Green House effect via use of recommendation models<br>11 Regression-based models in civil engineering<br>12 Application of ML in 3D Building Information Modelling (BIM)<br>13 Structural health monitoring system<br>14 Structural design and analysis
