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ITCS 4122: Visual Analytics

ITCS 4122: Visual Analytics. Jianping Fan Fall 2018. http://webpages.uncc.edu/jfan/itcs4122.html. Overview. Class hour 2:30PM - 5:15PM, Monday Office hour Monday 11:00 - 12:30PM, 5:30-7:00PM Classroom Woodward Hall 135 Instructor - Dr. Jianping Fan email - jfan@uncc.edu

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ITCS 4122: Visual Analytics

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  1. ITCS 4122: Visual Analytics Jianping Fan Fall 2018 http://webpages.uncc.edu/jfan/itcs4122.html

  2. Overview • Class hour 2:30PM - 5:15PM, Monday • Office hour Monday 11:00 - 12:30PM, 5:30-7:00PM • Classroom Woodward Hall 135 • Instructor - Dr. Jianping Fan • email - jfan@uncc.edu • Office – Woodward 205D • Webpage http://webpages.uncc.edu/jfan/itcs4122.html Textbook: we will use the slices and papers on the course web page TA for this class: Tinghao Feng, tfeng1@uncc.edu!

  3. Why we should have this course? • Internet & smartphones are changing the world

  4. Why we should have this course? • Various types of data (especially visual data) is dominating the content of Internet Big Data = Big Knowledge

  5. Why we should have this course? • Easy access (abstraction & interpretation) of multi-modal data through Internet & mobile devices could be the future of IT

  6. Why we should have this course? • This class will help you make these happen! Big Data = Big Knowledge • Data analysis for feature extraction • Machine learning for Data Abstraction & Knowledge Discovery • Visualization for data & knowledge layout and interpretation • Human advises for data analytics • Many others…..

  7. Why we should have this course? • Good job market: Google, Facebook.... • Have fun: solving real problem • Not so “hard” to learn (??) • Next generation applications

  8. Required Techniques How to build visual analytics tools? • Data Analysis Technologies for Feature Extraction • Machine Learning Tools for Knowledge Discovery (Abstraction) • Visualization for Data & Knowledge Interpretation • Human-Computer Interaction as Loop

  9. What is Visual Analytics? Loop?

  10. What is Visual Analytics? Loop?

  11. What is Visual Analytics? Loop?

  12. What is Visual Analytics? Loop?

  13. What is Visual Analytics? Visual Analytics = Visualization Visual Analytics = Data Analysis Data Analysis Visualization Visual Analytics =

  14. Components from Data Analysis • Data Analysis & Feature Extraction • Data Representation • Data Clustering & Classification for Knowledge Discovery

  15. Machine Learning

  16. Components from Visualization • High-Dimensional Data Visualization • Temporal Data Visualization • Graph & Tree Visualization

  17. Components from Interaction • Human-System Interaction • Understanding Human Interaction and Transfer them into computer understandable knowledge c. Leveraging Human Advices for second-loop data analysis

  18. Pre-Requirements of this Class • Data Analysis & Visualization • Computer Vision • Machine Learning • Programming Skills • Willing to work hard If you do not have these background, you should

  19. Course Topics • Data Clustering Tools • Machine Learning Techniques • Data Analysis Technologies • Deep Learning & Big Data Analytics • Visualization Tools • Human-Computer Interaction Tools • Open Discussion & Student Presentation

  20. Grading • Composition • Project 25% (excellent implementation can be up to 45%) • Midterm & Final 75% • Scale • >93% = A • 75-93% = B • 55-74% = C • <55% or cheating = F If you miss 3 classes (three weeks) or more, you are not allowed to take tests (mid-term and final)!

  21. Class Policy • You have to attend the class & come to classroom on time (no later than 2:35pm) • You should be ready to learn from the class: project implementation could be critical • You should respect your classmates: come to learn from their presentation!

  22. Classroom Policy • No food!!! Drink could be allowed & Cell Phone should be turned off. • Small talk is not allowed, but you are welcome to ask questions! • Walking inside classroom is not allowed within presentation time!

  23. Course Projects You need to do project implementation and presentation • Project implementation project: you need to set up a team or individual to implement one small system for visual analytics. • More information • http://webpages.uncc.edu/jfan/itcs4122.html

  24. Implementation Project • Develop visual analytics system using Visual C++ and Java. • Each group consists 3-4 students • 3-4 hours workload each week is expected • Java or C++ assumed • Talk to your professor to decide which algorithm you may implement for your project, discuss progress with your professor if necessary • Demonstrate your implementation to your professor & get feedback

  25. Course Projects If you do wonderful job on course project, you may expect: • Good grade even you may perform well in final and mid-term tests • Practical implementation means more than paper work • Good recommendation letter for job hunting: professor can only memorize students with good performance! • Research position opportunities

  26. Midterm & Final • closed books and notes • One page notes is permitted • Cumulative • No makeup • Bonus is expected • Key components for your final grade If you miss 3 classes or more, you are not allowed to take tests (mid-term and final)!

  27. Suggestions from Instructor • Do your best in the class • Show your problems to the instructor when you cannot make it • Show the evidence to us if you think you are right. • Open discussion is welcome, but no small talk

  28. 10-hours Golden Rules • 3 hours before class: go through the topics, presentation slides and seek some relevant online documents, …; ready to ask questions in class • 3 hours in class: listen to domain experts and try to ask questions • 4 hours after class: review what you have learnt from the class, do your project and assignments…

  29. Recommendation • Good grade is very important, but it is not everything! • Learning something and solving one problem you like may be more important! • Learning from someone who may make you better! Especially your classmates

  30. What areas we will touch? • Data Analysis Tools for Feature Extraction • Machine Learning & AI (Deep Learning) • Visualization • Interaction between data/knowledge analytics and visualization

  31. Why We Need Visual Analytics? Better Data Interpretation

  32. Why We Need Visual Analytics? Better Data Interpretation

  33. Why We Need Visual Analytics?

  34. Why We Need Visual Analytics?

  35. Why We Need Visual Analytics? Better Data Interpretation

  36. Why We Need Visual Analytics?

  37. Why We Need Visual Analytics?

  38. Why We Need Visual Analytics?

  39. Why We Need Visual Analytics?

  40. Why We Need Visual Analytics? Better Evaluation

  41. Why We Need Visual Analytics? Better Evaluation

  42. Why We Need Visual Analytics? Better Evaluation

  43. Why We Need Visual Analytics? Better Evaluation

  44. Why We Need Visual Analytics? Better Evaluation

  45. Why We Need Visual Analytics?

  46. Why We Need Visual Analytics?

  47. Sailing Red Flower Red Flower Sailing

  48. why not ask "stupid" questions? Do your best & have fun! Good students should be able to push your professor to think and work harder not easier!

  49. I am a nice professor if you do your jobs!

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