Day 2, R2 11:45–12:15
Blackbox problem has been becoming a popular concern when applying machine learning in specific applications, like medical system, where a user is supposed to understand the behavior of the system. Collecting tons of data for training machine learning model is another headache especially when you newly create a system from scratch. In this talk, I introduce the data analysis approach called "Sparse Modeling" that can produce good results, even if the amount of data is small. Event Horizon Telescope project, capturing blackhole image, is one good example of this nature. It's also referred to as explainable since it can tell you which input features have a strong impact to result generated by a machine learning model. With the overview of the method, I'll show concrete code examples for common use cases like image analysis, using a Python library named spm-image.
Speaker: Takashi Someda
After getting his master’s degree in informatics at Graduate School of Kyoto University, he started his job at Sun Microsystems as an engineer.
For about 20 years in the software industry, he has experienced several roles like software developer, technical evangelist, and data scientist.
Now, as CTO of Hacarus, he is responsible for technical direction with strong passion toward building a creative, self-organized team like Pixer.