Lecture

Sustainable Manufacturing: Design & Decision-Making

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Circularity & Sustainability in Industry 4.0

Delves into methods for advancing circularity and sustainability in Industry 4.0 through industrial ecology, sustainable design, and digital technologies.

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Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

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Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

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Explores data representations, histograms, neural networks, and deep learning concepts.

Circular Economy for Sustainable Composites

Delves into sustainability strategies, the circular economy, and challenges in the composites industry, addressing climate change effects and the role of composites.

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Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

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Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

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Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.