Applied Machine Learning

Catalog description

This course covers the theory and technologies related to applied machine learning. In particular, the course focuses on the analysis of data produced via web applications and social media. Topics to be covered include: data pre-processing; machine learning in the context of Big Data, social media, information retrieval; web crawling and data scraping; features and feature extraction; vector space models and analysis; data mining of unstructured data in the web; traditional machine learning methods for data analytics; introduction to deep learning; machine learning evaluation methods; and other special topics. Permission of instructor required. Course Learning Outcomes 1. Understand the basic machine learning pipeline. 2. Understand how to extract data from social media and the web. 3. Understand machine learning in the context of big data. 4. Understand features and feature extraction techniques. 5. Understand the vector space model. 6. Understand traditional machine learning methods. 7. Understand deep learning. 8. Understand common machine learning evaluation methods. 9. Collaborate with team members to resolve machine learning problems. 10. Conduct independent research under the instructor’s guidance. View Class Schedule