Welcome to the IKCEST
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Big Data





Opening Ceremony



Introduction to Big Data and Deep learning

Lyu Na


From Quarantine to Cloud Computing

Shi Bin


Introduction to Machine Learning  

Luo Minnan


Frontier of Cloud - Green Data Center

Shi Bin


Network Representation Learning and Its Applications

Luo Minnan


Introduction to Big Data Platforms and Applications of Big Data Technology

Tian Feng


An introduction to IKCEST



Grad Ceremony(Online Assessment, Online Examination)



Big Data Processing and Deep Learning

Lecturer: Lv Na

Abstract: This course will introduce the methods and frontiers in big data processing and analysis, machine learning, deep learning and data mining. The representative big data processing platform Hadoop and the classic big data analysis mechanism Map-reduce will be introduced. The foundations of machine learning and artificial intelligence will be discussed, including linear regression, logistic regression, neural networks, other supervised learning and unsupervised learning methods. The concept and motivation of deep learning will be discussed. The breakthrough methods in deep learning, including auto Encoder, Restricted Boltzmann Machine, and Convolutional Neural Network will be introduced. Take the deep learning research in Google Inc. and recommendation system as examples, the applications of deep learning and big data analysis will be introduced.


Lecturer:Lv Na received her B.S. and Ph.D. degrees from Xi’an Jiaotong University, Xi’an, Shaanxi, and China in 2002 and 2008, respectively. She received postdoctoral training from University of Rochester, USA in 2011 to 2013. Currently, she is an Associate Professor at Xi’an Jiaotong University, Xi’an, Shaanxi, China. She is the dean of Advanced Robot Control Laboratory of Shaanxi Province. She has published more than 30 papers in the top journals including IEEE Transaction on Pattern Analysis and Machine Intelligence、IEEE Transaction on Neural System and Rehabilitation Engineering、Pattern Recognition and so on. Her research interests include big data processing, statistical image analysis, machine learning, cognitive science and robotics.


From the quarantine to cloud computing

Lecturer: Shi Bin


Abstract: Cloud computing is a kind of distributed computing. It refers to the decomposition of huge data calculation processing programs into countless small programs through the network "cloud". Then, through the system composed of multiple servers, processing and analyzing these small programs to obtain the results and return to the user. After ten years of development, cloud computing has become a very mature and efficient technology, and has achieved very good results in terms of computing power and energy consumption. In the COVID-19 epidemic, a large number of students began to use online classrooms for learning, and the demand for computing power surged. Cloud computing solves this problem elegantly through its elasticity and dynamic characteristics. This lecture will introduce the basic principles, features and advantages of cloud computing. Then introduce the research of the latest frontier areas, pointing out the current open issues and possible directions of cloud computing. Through this lecture, it will help to better understand cloud computing and learn about the latest research hotspots.

LecturerBin Shi, Ph.D., working in the School of Computer Secience and Technology, Xi'an Jiaotong University. Research field: cloud computing, system security, system for AI.



Introduction to Machine Learning

Lecturer:Luo Minnan

Abstract:Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. In this course, we will talk something about machine learning, including the evolution of machine learning, why is machine learning important, and some popular machine learning methods and its applications.


Lecturer:  Luo Minnan received the Ph. D. degree from the Department of Computer Science and Technology, Tsinghua University, China, in 2014. She was a Post-Doctoral Research with the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. She is currently an Associate Professor in the Department of Computer Science and Technology at Xi'an Jiaotong University. Her research interests include machine learning, and its applications such as image processing, data mining and information retrieval.


Network Representation Learning and Its Applications

Lecturer: Luo Minnan

Abstract:A variety of data in many different fields can be described by networks, such as the World Wide Web, social networks of acquaintances or other types of interactions, networks of publications linked by citations, transportation networks, metabolic network, and communication networks. In this talk, we will recall some basic concepts and preliminaries of network in mathematics, and then introduce the representation learning of network in the framework of machine learning, together with some applications.


Brief introduction to Big Data Technology and platforms
Lecturer: Tian Feng

Abstract:This talk gives a brief introduction to big data technology and platforms. To be specific, history, original, and supporting technology of the era of big data are reviewed. Next, shows four paradigms of experiment, theory, calculation and data in scientific research. Then, discusses the related topics on AI, Data Science, and data mining, as well as supervised and unsupervised method. Finally, briefly describes a few big data platforms, for an example, MapReduce.  



Several applications of Big Data Technology
Lecturer: Tian Feng

Abstract:This talk gives several applications of Big Data Technology in the fields, such as disease control, education. To begin with, introduce COVID-19 and control plan of China. Next, introduce a real-time monitoring big data platform of higher education teaching quality. 


Lecturer: Tian Feng, the professor and doctoral supervisor of the Department of Machine Intelligence, the Faculty of Electronic and Information Engineering. He is also a member of IEEE, the director of Big Data Algorithm Testing and Application Demonstration Center of National Engineering Laboratory for Big Data Algorithm and analysis technics. What’s more, he is a member of Key Laboratory of intelligent network and network security, Ministry of Education (Xi'an Jiao Tong University), and a member of Shaanxi Key Lab. Of Satellite-terrestrial Network Tech. R&D. He has visited the University of Bradford and Coventry University several times. In recent five years, his research directions include Big Data Mining Application, Smart Education and Artificial Intelligence, Multi-mode Data Mining and Pattern Recognition, Affective Computing, Cloud Storage and Modeling and Analysis of Complex System, and Personalized Service in Intelligent Network Learning Environment. In the past three years, his research directions were Multi-agent System, Petri Net and Its Application and Intelligent Wheelchair.


Frontier of Cloud - Green Data Center

Lecturer: Shi Bin

Abstract: In recent years, the concept of green world, environmental protection and sustainable development has gradually become popular and penetrated into all walks of life. This is no exception for computer systems, and the concept of the green data center is proposed under this background. The meaning of the green data center is to improve the energy efficiency of the data center, minimize the overall power consumption of the data center, maximize the proportion of the IT system in the overall data center power consumption, minimize the power consumption for non-computing equipment (power conversion, cooling, etc.). Green data center has become an important concept and goal of cloud computing technology development. This lecture introduces the relevant technologies of green data center, including virtualization technology, data center scheduling technology, and virtual machine migration technology. This lecture will help you better understand cloud computing and data center management, and current research hotspots.


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