Introduction to Machine Learning. The knowledge discovery process. Data preprocessing. The problem of learning. Supervised and unsupervised learning. Batch, incremental, natural learning. Reinforcement learning. Problems related to learning: parameter tuning, performance evaluation, training, validation and testing, the problem of overfitting. Classification: decision trees. Linear and logistic regression. Artificial neural networks. Clustering: K-Means. Agglomerative and density-based clustering (DBSCAN). Representation learning. Convolutional Neural Network. Recurrent Neural Network. Long Short-Term Memory Network. Introduction to the Python language. Python and the Jupyter Notebook environment. The Sikit-learn environment: exercises on supervised classification for construction engineering.
The TensorFlow environment: exercises on Convolutional Neural Network and Recurrent Neural Network for construction engineering.
Libraries for parallel computing: exercises for construction engineering.