+Data Science (+DS) is a Duke-wide program, operating in partnership with departments, schools, and institutes to enable faculty, students, and staff to employ data science at a level tailored to their needs, level of expertise, and interests. For more information, please visit our website at https://plus.datascience.duke.edu.
Upcoming Virtual Learning Experiences
In February, please join us for a trio of foundational sessions: Introduction to machine learning, natural language processing, and image analysis. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.
Introduction to Basic Concepts in Machine Learning
Monday, February 8 | 12:00-1:00 PM
David Carlson
The basic concepts of machine learning are introduced with a focus on intuition and examples. The simpler and widely used logistic regression model is introduced first, and from this, multilayered neural network are introduced as a generalization. Concepts of parameter learning (optimization), generalization (and overfitting), validation and performance evaluation are also introduced. Register at https://training.oit.duke.edu/enroll/common/show/21/175395
Natural Language Processing with LSTM Recurrent Neural Networks
Tuesday, February 16 | 4:30-5:30 PM
Ricardo Henao
Natural language processing (NLP) is a field focused on developing automated methods for analyzing text, and also for computer-driven text generation (synthesis, for example in translation and text summarization). Recurrent neural networks have recently become a state-of-the-art method for NLP, with the long short-time memory (LSTM) network representing the primary methodology of this type. In this session LSTM NLP models will be introduced, with as little math as possible and with an emphasis on intuition. The concept of word embeddings will be introduced within the context of implementing LSTMs, and it will be explained how such models are used in practice for analysis and generation of natural language (e.g., language translation). Register at https://training.oit.duke.edu/enroll/common/show/21/175397
Convolutional Neural Networks for Image Analysis
Wednesday, February 24 | 4:30-5:30 PM
Tim Dunn
The convolutional neural network (CNN) represents the current state-of-the-art for image and video analysis, and is increasingly used for analyzing time series and other data with spatial or sequential structure. This session will provide an intuitive introduction to the fundamentals of CNNs, with an emphasis on hierarchical feature extraction and the convolution operation itself. Model training and transfer learning will also be discussed. Register at https://training.oit.duke.edu/enroll/common/show/21/175394
Updates from the Center for Computational Thinking
The Duke Center for Computational Thinking (CCT) prepares Duke graduates to apply computational thinking to ideas, challenges, and opportunities while simultaneously considering the ethical, legal, and social impacts of technology on humans for the betterment of society. +DS is one of the partner programs supporting the CCT mission. To learn more, please visit https://computationalthinking.duke.edu/.
Digital Intelligence (DQ): A new undergraduate certificate track
The Digital Intelligence certificate is a new track within the Science & Society undergraduate certificate program. This new track focuses on providing undergraduate students with foundational understanding of contemporary and emerging computational thinking, such as artificial intelligence, cybersecurity, web/mobile technologies, databases and software engineering. This training in technology will be coupled with requisite ethical, legal, social, and policy frameworks needed to understand the complexities of technology’s impact on our world. The Digital Intelligence track provides students with a holistic understanding of revolutionary technologies and the responsibility researchers, designers, and regulators bear in ensuring they are built and implemented responsibly.
Learn more at https://computationalthinking.duke.edu/dq-certificate/
