Brief Introduction
Learn how to discover domain structure for knowledge inference.Description
In this course, you will learn key methods for discovering how content can be divided into skills and concepts and how to measure student knowledge while it is changing – i.e. the student is learning.This course will also cover related methods for discovering structure in unlabeled data, such as factor analysis and clustering. It will also cover related methods for relationship mining including how to validly conduct correlation mining and how to automatically discover association rules and sequential rules.
This mini-course does not assume prior programming knowledge beyond what you will already have learned in other courses in this MicroMasters, although advanced tools will be discussed for interested students.
This course includes content also offered in the University of Pennsylvania’s edX MOOC, Big Data and Education, weeks 4, 5, and 7.
Knowledge
- Domain structure discovery (how to map content to skills/concepts)
- Knowledge inference (calculating what a student knows)
- Cluster and Factor Analysis
- Correlation Mining
- Association and Sequential Pattern Mining
Learn Data Science Deep Learning, Machine Learning NLP & R
- 4.5
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Learn Data Science, Deep Learning, Machine Learning, Natural Language Processing, R and Python Language with libraries || DATA SCIENCE || What Does a Data Scientist Do?...
Mathematics & Statistics of Machine Learning & Data Science
- 4.2
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Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning || DATA SCIENCE || What Does a Data Scientist Do?...
Knowledge Inference and Structure Discovery for Education
- 0.0
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Learn how to discover domain structure for knowledge inference. e. the student is learning. Domain structure discovery (how to map content to skills/concepts)...
