Artificial Intelligence Frontier Research Topics: Multidimensional Applications Of Machine Learning And Deep Learning

Course Arrangement

Enrollment status: Enrolling

Course time: rolling classes

Course method: Zoom remote live teaching

Course schedule: 21 class hours for the main academic tutor + 9 class hours for the academic associate tutor + 6 class hours for the main thesis tutor + 21 class hours for the deputy thesis tutor, lasting 7 weeks

Course Description

Deep learning (DL, Deep Learning) isa new research direction in the field of machine learning (ML, Machine Learning). It is introduced into machine learning to make it closer to the original goal - artificial intelligence (AI, Artificial Intelligence). Machine learning is a subfield of artificial intelligence and the core of artificial intelligence. It encompasses almost all the methods that have the greatest impact on the world. Machine learning theory mainly involves the design and analysis of algorithms that allow computers to learn automatically. Deep learning is a subcategory of machine learning. It is inspired by the way the human brain works and is a learning process that uses deep neural networks to solve feature expressions. Deep neural network itself is not a new concept. It can be understood as a neural network structure containing multiple hidden layers. In order to improve the training effect of deep neural networks, people have made adjustments to the connection methods of neurons and activation functions. Its purpose is to establish and simulate the human brain's neural network for analysis and learning, and to imitate the human brain's mechanism to interpret data, such as text, images, and sounds.

Suitable For

High school students and undergraduates interested in artificial intelligence, neural networks, and deep learning

Students majoring in computer science, programming, information engineering, etc., as well as students who want to work in the fields of machine learning, algorithms, programming, pattern recognition, etc. in the future

Students with a certain foundation in calculus, linear algebra, and programming are preferred

Professor Introduction

Tenured professor at Imperial College

Björn Schuller

Tenured Professor of Computer Science at Imperial College London

Head of the Languages, Audio and Music Group in the Department of Computing at Imperial College London

Chief Scientific Officer and Co-Founder of audEERING GmbH, Germany

One of the 40 Outstanding Young Scientists of the World Economic Forum in 2015-2016

Visiting Professor, School of Computer Science and Technology, Harbin Institute of Technology

Honorary Dean of Tianjin Normal University Emotional Intelligence Center

Number of citations: 53473; h-index: 102; i10 index: 642

TA Introduction

Educational background: Master/PhD background from TOP30 prestigious universities at home and abroad, over 40% of the team is PhD students

Professional excellence: Have experience in publishing papers in relevant professional fields

Rich experience: Excellent academic performance & fluent in English, with many years of experience as a teaching assistant

Teaching orientation: Seamlessly connect professor topics and participate in teaching and delivery throughout the process

Program Advantages

Complete course hours that meet the academic program requirements of colleges and universities

A complete scientific research project under the personal guidance of the professor

A true and valid recommendation letter personally issued by the professor