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Machine Learning (ML) nowadays has been attached great attention by academic institutions, intelligent manufactories, and digital business sectors. As a cutting-edge subject of AI, ML has been widely studied and developed by a large group of notable scholars and practitioners and has a predictable application foreground in the region of business, industry, spaceflight and entertainments. “What is Machine Learning?” would be the fundamental learning objective of the MLSS program. Diagnosing disease, predicting weather events, identifying fraud, and personal assistants who anticipate our next desire (Alexa, Siri, Cortana, etc.), we have all heard about the new amazing tasks machine learning can do. However, do you understand how these algorithms work and when we can expect them to work and, perhaps more importantly, when and why they fail? As a data scientist or machine-learning practitioner, you will be asked to explain how your algorithm works when it works and why your algorithm fails. We will discuss reasons why algorithms may fail in this summer. Upon successful completion of the one-week program, participants will receive a certification of attendance delivered by Duke Kunshan University.
The goal is to provide you with a foundation in fundamental concepts ubiquitous to machine learning – classification, regression, model selection, bias-variance trade-off by discussing these concepts within the context of foundational machine learning algorithms and techniques. As this course emphasizes fundamental concepts in machine learning, we will not discuss advanced topics such as machine learning theory, reinforcement learning, deep learning, or adversarial networks. By the end of this course, you will be prepared to apply machine learning algorithms appropriately and effectively (as opposed to treating them as a set of black box options) and will understand the strengths and limitations of the foundational machine learning algorithms. You will also understand how the fundamental concepts are widely applicable and will be better prepared to apply them in the context of more advanced machine learning algorithms when you take advanced classes. With respect to specific course learning objectives, you will be able to: (1) identify opportunities for the use of machine learning techniques; (2) discuss potential approaches and challenges for specific applications; (3) design algorithmic workflows for specific applications; (4) implement machine learning algorithms by listing their detailed steps.
What is Machine Learning?
Linear Regression, Convex Optimization Regularization, Classification, Support Vector Machine, Logistic Regression, K-nearest Neighbors Gradient Method, Newton Method, Lagrange Multiplier, Interior Point Method Monte Carlo Analysis, Latin-hypercube Sampling, Principal Component Analysis, Dimension Reduction Kernel Function, Decision Tree and Random Forest, Clustering.
Dr. Li is a full Professor in the Department of Electrical and Computer Engineering at both Duke University and Duke Kunshan University. Dr. Li received his PhD in Electrical and Computer Engineering from Carnegie Mellon University (2005), and his MS and BS degrees in Electronics Engineering from Fudan University. In 2005, he co-founded Xigmix Inc. to commercialize his PhD research, and served as the Chief Technical Officer until the company was acquired by Extreme DA in 2007. In 2011, Extreme DA was further acquired by Synopsis (Nasdaq: SNPS). From 2009 to 2012, Dr. Li was the Assistant Director for Focus Research Center for Circuit & System Solutions (C2S2), a national consortium of 13 research universities (CMU, MIT, Stanford, Berkeley, UIUC, UMich, Columbia, UCLA, among others) to work on next-generation circuit/system design challenges. His research interests include integrated circuits, signal processing and data analytics. Dr. Li was the Deputy Editor-in-Chief of IEEE TCAD. He was an Associate Editor of IEEE TCAD, IEEE TBME, ACM TODAES, IEEE D&T and IET CPS. He was the General Chair of ISVLSI and FAC. He received the NSF CAREER Award in 2012 and six Best Paper Awards from IEEE TCAD, DAC, ICCAD and ISIC. He is a Fellow of IEEE.
