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Advanced Machine Learning and its Applications

University of Glasgow via Coursera

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Overview

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The third course introduces advanced machine learning techniques and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of machine learning, and Course 1, which developed LLM-empowered Python programming skills for AI. In this course, advanced data preprocessing, machine learning outcome evaluation, neural network design and optimization, deep learning, and generative artificial intelligence are introduced. Since these techniques underpin essential modern AI systems in science and engineering, after completing this course, you will be able to develop, evaluate, and deploy advanced AI solutions using Python and LLM tools, providing a solid foundation for tackling real-world problems across a wide range of scientific and engineering domains.

Syllabus

Advanced Data Preprocessing: Feature Selection and Dimension Reduction
- This module introduces advanced data preprocessing techniques that improve data quality and enhance machine learning performance. It covers data cleaning, missing value processing, anomaly detection, data normalization, data encoding, feature selection, and dimensionality reduction. Python implementation, assisted by an LLM, and real-world case studies are integrated throughout the module. After learning this module, students will be able to:
Evaluating Machine Learning Outcomes
- Reliable model evaluation is essential for developing trustworthy machine learning systems. This module introduces evaluation methodologies for different machine learning scenarios, including traditional model evaluation, sequential data evaluation, unstable model evaluation, imbalanced learning, and statistical comparison of machine learning models. Python implementation, assisted by an LLM, and practical case studies are incorporated throughout the module. After learning this module, students will be able to:
Neural Networks Design and Tuning
- This module introduces the design principles and optimization techniques of neural networks, including weight initialization, optimization algorithms, normalization methods, regularization strategies, and data augmentation techniques. Python implementation using modern deep learning frameworks and LLM-assisted programming are integrated with practical engineering examples. After learning this module, students will be able to:
An Introduction to Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks particularly LSTM
- This module introduces modern deep learning architectures for processing images and sequential data, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRU), attention mechanisms, and Transformers. Python implementation assisted by LLM and representative AI applications are covered throughout the module. After learning this module, students will be able to:
An Introduction to Generative AI (GAN, VAE, Transformer, and Diffusion Models)
- This module introduces the principles and applications of modern generative artificial intelligence, including traditional generative models, variational autoencoders (VAEs), normalizing flows, generative adversarial networks (GANs), and diffusion models. Python implementation assisted by LLM is combined with representative real-world case studies. After learning this module, students will be able to: