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CSE590/MB590 Special Topics
  - Data Visualization for Machine Learning

This course is an introduction to Data visualization and communication using Machine Learning and its core models and algorithms for students in the Data Science Program. It covers all significant topics, including graphics, discrete and continuous variables, clustering and classification. The objective of the course is to provide students an overview of machine learning techniques to visualize and explore, analyze, and leverage data.
  • » 23 hours (8 weeks) in class lecturing plus dedicated mentoring sessions from our faculty of industry experts
  • » 1.5 semester credits for both certificate and master’s degree
  • » Access to high-quality live class recording
  • » Online live classroom available for all classes
  • » Lifetime learning resources for our students
  • $ 990
Course Description

This course is an introduction to Data visualization and communication using Machine Learning and its core models and algorithms for students in the Data Science Program. It covers all significant topics, including graphics, discrete and continuous variables, clustering and classification. The objective of the course is to provide students an overview of machine learning techniques to visualize and explore, analyze, and leverage data.

The course covers the use of data analysis and machine learning to aid the development of visualization. Implement prototypes that use visualization to explain machine learning models supervised, unsupervised, and reinforcement learning. Students will be familiarized with broad machine learning and statistical pattern recognition topics, including: neural network training, classification, regression, support vector machines.

This course will use different languages' frameworks to demonstrate machine learning techniques. Students will use R and Tableau to complete the homework, assignments and projects through the course.

Measurable Course Objectives

Students will be able to:

  • Design and create data visualizations using Machine learning algorithms and concepts
  • Conduct exploratory data analysis using Machine Learning techniques for this course we will use R and Tableau.
  • Understand the core concept of data visualization in supervised, unsupervised learning and reinforcement learning.
  • Use data with visual representations and craft visual presentations of data for effective communication.
University-wide Student Learning Outcome

The University Student Learning Outcomes assessed and reinforced in this course include but are not limited to the following:

  • Communication
  • Critical Thinking
  • Information Literacy
Course Topics
  • Week 1: Introduction to Visualization and Machine Learning.
  • Week 2: Programming Languages (R and Tableau Introduction).
  • Week 3: Data Pre-processing and Visualization for Machine Learning Models.
  • Week 4: Visualization Exploratory Data Analysis and Machine Learning Mathematics.
  • Week 5: Subspace clustering, regression and visualization.
  • Week 6: View transformation, Classifier Decision Tree
  • Week 7: Visualize multi-layer network and Deep Learning
  • Week 8: Visualizing and Model Evaluation and Final Project
Objectives

To accomplish its mission, CSTU is committed to enhance student competencies by:

  • Providing working adults with higher educational opportunities that are flexible and accessible;
  • Providing graduate level students professional training and preparation for careers
  • Providing higher educational opportunities that are current with technology and career demands;
  • Providing faculty members that have demonstrated expertise in, their respective domain, both professionally and academically;
  • Integrating into the educational process a better understanding of cultural diversity needs;
  • Delivering educational support services that meet student life demands and schedules;
  • Building within students a value for life-long learning and education;
  • Providing educational resources in a manner that effectively uses current technology.

CSTU is committed to the highest ethical standards in the pursuit of the mission. The policies, procedures, and standards guide CSTU core values set forth below. These values are honored in our daily structure and activity as members of this community. We are committed to respect the rights and dignity of others while conducting ourselves with integrity in our dealings with and on behalf of all individuals in our environment and are accountable as individuals and as members of this community for the ethical conduct and for compliance with applicable laws, University policies, and directives while conscientiously strive for excellence in our work.

About the Instructor

Dr. Glen Qin

got his Ph.D. degree from UC Berkeley in EECS, and have been working in the industry for about 20 years. The industrial experience at Silicon Valley has helped Dr. Qin always be at the cutting edge of the new technologies. He has been working for large companies, like AT&T, small startups, like Optimal Networks, and medium size company like Netgear. His industrial experience is mainly focused on Computer Networks, Internet of Things, Cloud Computing, big data, and AI. He started his career as a software engineer and moved to engineer management, and product management gradually. In 2011, Dr. Qin and a few others founded California Science and Technology University (CSTU), and has been the president of the CSTU ever since.

Dr. Qin is also actively involving in community activities. He was the president and the chairman of Silicon Valley Tsinghua Network, a Tsinghua Alumni organization helping the graduates from Tsinghua to thrive in the Silicon Valley.