Artificial Intelligence: The Multidimensional Application of Machine Learning and Deep Learning

Course 18: Artificial Intelligence: The Multidimensional Application of Machine Learning and Deep Learning

I. Course Description

Artificial intelligence is the development of computer systems that can perform tasks that normally require human intelligence. AI has applications in a wide range of areas, including healthcare, finance, transportation and entertainment, and has the potential to change the way we live and work. In addition, generative AI is an artificial intelligence capable of creating new and original content, such as images, music, or text. Today, AI uses deep learning technologies such as convolution and recurrent neural networks, generative adversarial networks (GAN), variational autoencoders (VAE) and converters to analyze data and generate output that mimics the style or features of a given input.

This course aims to provide a basic understanding of the fundamental technologies that underpin AI, from basic methods to the latest technologies of deep learning. AI involves using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data or to generate such knowledge and insights. On the other hand, machine learning, as a major subfield of AI, allows machines to generalize from observations during the training phase. For the latter, they are usually presented as “labeled” examples, where data points, including the machine must later analyze or synthesize its own information in a new unknown example.

II. Professor Introduction

Björn Schuller – Tenured Professor at Imperial College London

Professor Bjorn Schuller is a prominent leader in the field of artificial intelligence, especially in emotional computing and machine learning for audio and voice analytics. He is known for his research on the use of machine learning algorithms to analyze human emotions, personality traits, and mental health through speech and other patterns. Schuller Has published numerous papers in top journals and conferences, and has won many awards for his research results. Professor Schuller is also known for his work on the development of speech and emotion recognition applications, including healthcare, human-computer interaction, and social robotics.

In addition to his research work, Professor Schuller participates in a variety of academic and professional activities. He was a professor at the University of Augsburg in Germany and an adjunct professor at Passau University and Imperial College London. Bjoern Schuller Is a key member of the IEEE, the International Voice Communications Association (ISCA) and the European Academy of Sciences, and has received several European Research Council (ERC) grants to support his innovative research in artificial intelligence and machine learning. Professor Schuller, a leading researcher in the field of emotional computing, has been listed as a highly cited researcher by Clarivate Analytics, indicating that his work has had a significant impact on the development of technologies that can understand and respond to human emotions.

III. Syllabus

1. Deep feedforward neural network
2. Test of deep neural network; convolutional neural network
3. recurrent neural network
4. Connect timing classification for time series management
5. end-to-end learning (e2e)
6. generative adversarial network (GANs)
7. Transfer learning; weak supervised learning
8. Enhanced learning; green learning and joint learning
9. 9 for applications in the analysis of different signals
10. Application in natural language processing