This course provides an overview of some different concepts underpinning Generative AI, their mathematical principles, and their applications in engineering. The focus will be on the practical implementation of generative AI including, neural networks, attention mechanism, and advanced deep learning models.
Generative AI: Foundations and Concepts
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
- Bayesian Network
- Statistical Methods
- Artificial Neural Networks
- Machine Learning Methods
- Generative AI
- Probability & Statistics
- Applied Mathematics
- Network Architecture
- Probability Distribution
- Machine Learning
- Deep Learning
- Computer Vision
- Artificial Intelligence and Machine Learning (AI/ML)
- Mathematical Modeling
Details to know
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Recently updated!
April 2025
Assessments
9 assignments
Taught in English
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There are 4 modules in this course
In this module, you will explore the foundations of neural networks, including perceptrons, architectures, and learning algorithms. You will dive deeply into optimization methods critical for efficient training, focusing on advanced techniques like Newton’s and quasi-Newton methods, momentum, RMSProp, and Adam optimization algorithms.
What's included
6 videos15 readings2 assignments2 discussion prompts
This module guides you through the mathematical approaches to regularization techniques that enhance neural network generalization and prevent overfitting. You will analyze concepts including Stein’s unbiased risk estimator, eigen decomposition, ensemble methods, dropout mechanisms, and advanced normalization techniques such as batch normalization.
What's included
4 videos17 readings2 assignments1 discussion prompt
In this module, you will examine convolutional neural networks (CNNs), including convolution operations, parameter sharing, kernel methods, and multi-dimensional data structures. You'll explore advanced CNN architectures, regularization, normalization techniques, and the implications of random kernels on network learning behavior.
What's included
5 videos31 readings2 assignments1 discussion prompt
In this module, you will analyze the maths underpinning generative models and maximum likelihood estimation (MLE). You will explore divergence metrics such as Kullback-Leibler divergence, Bayesian network structures, and autoregressive modeling methods, focusing on their theoretical foundations and practical implications.
What's included
6 videos33 readings3 assignments1 discussion prompt
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