Description

This course provides a comprehensive introduction to neural networks and their applications in solving complex real-world problems. It covers the fundamental concepts of artificial neural networks, from basic architectures and shallow learning to advanced deep learning techniques. Students will explore key topics such as backpropagation, optimization methods, and regularization strategies to build and train robust models. 

The course emphasizes both theoretical foundations and practical implementation, including state-of-the-art techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Additionally, students will gain insights into cutting-edge topics such as reinforcement learning with human feedback, time series analysis, and natural language processing (NLP) with large language models (LLMs). 

Through hands-on projects and case studies, students will develop the skills to design, train, and evaluate neural networks for a variety of applications, including image processing, audio analysis, and generative AI. By the end of the course, participants will be equipped with the knowledge and tools to tackle challenges in machine learning research and industry. 

Recommended textbooks 

[1]Text Analytics with Python A Practitioner’s Guide to Natural Language Processing  
[2] Getting started with natural language processing