Advanced Machine Learning: Fundamentals and Applications
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Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores quantum chemistry applications, emphasizing the role of electron density in predicting chemical properties and addressing challenges in catalyst design, solar energy conversion, and drug synthesis.