Principles of Modeling

Author(s): Marten Lohstroh, Patricia Derler, Marjan Sirjani

Series: Lecture Notes in Computer Science 10760

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Publisher: Springer International Publishing, Year: 2018

ISBN: 978-3-319-95245-1,978-3-319-95246-8

This Festschrift is published in honor of Edward A. Lee, Robert S. Pepper Distinguished Professor Emeritus and Professor in the Graduate School in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, USA, on the occasion of his 60th birthday.

The title of this Festschrift is “Principles of Modeling" because Edward A. Lee has long been devoted to research that centers on the role of models in science and engineering. He has been examining the use and limitations of models, their formal properties, their role in cognition and interplay with creativity, and their ability to represent reality and physics.

The Festschrift contains 29 papers that feature the broad range of Edward A. Lee’s research topics; such as embedded systems; real-time computing; computer architecture; modeling and simulation, and systems design.

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Lecture 1: Introduction to Big Data Mining

Basic concepts, history and some examples of data mining.

Feb. 27, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 3: Fundamental Data Mining Algorithms

Frequent patterns, association rules, Apriori, FPGrowth, KNN.

Mar. 13, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 2: Know Your Data

Data representation, visualization and proximity measures.

Mar. 6, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 5: Supervised Learning (Part II)

Support Vector Machines, Neural Networks.

Apr. 3, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 4: Supervised Learning (Part I)

Intro to machine learning, linear regression and logisitic regression.

Mar. 20 and 27, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 7: Unsupervised Learning

K-means Clustering, PCA, Mixture Gaussian, EM Methods

Apr. 17, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

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Lecture 8: Search Engines

Information retrieval, inverted index, retrieval model, relevance model

May 8, 2019

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EE448 Big Data Mining 2019

Weinan Zhang, Assistant Professor

John Hopcroft Center for Computer Science

SEIEE

Shanghai Jiao Tong University

Email: wnzhang [AT] sjtu.edu.cn

Big data driven techniques have been revolutionizing various aspects of our daily life. Big data means not only big volume but also high dimension and diversity. How to collect, represent, process and compute so as to successfully mine valuable patterns and acquire benefit from the big data is a fundamental challenge to both academia and industry.

This course provides a comprehensive introduction of the fundamental problems and methodologies of big data mining. The organization of the course would be application oriented, which helps SEIEE students get familar with various data mining tasks and basic solutions. Via lectures, hands-on courseworks and poster presentations, the students are expected to acquire the basic theory, algorithms, and some practice experience of big data mining techniques. It would also help students find their interested research topics, which could benefit their further graduate study and industrial practice.

收起