Whenever we advance our understanding of the world, we write down our findings in blog posts, publications, patents, webpages or the like. This results in a vast and ever-increasing body of literature, impossible to effectively access and comprehend. One of the overarching goals of Machine Learning research is to extract and distill this knowledge.
The primary aim of the session is to give an overview of the tools and techniques used when dealing with ML problems as well as to get to know the types of issues people face when dealing with learning related problems followed by discussing possible solutions for the same.
- >> The first part of this session would be devoted to discussing some prominent Machine Learning topics (in brief) along with looking at some cool projects going on around the world.
- >> This would be followed by more realistic phase wherein we would discuss what common learning problems do people (organizations, start-ups, others) face.
- >> Towards the end of the session we would try to have a look at the existing tools (primarily open-sourced) which one could use off-the-shelf and solve the problem at hand.
The presentation would span a maximum of 10-15 minutes. The creamy part of the session should ideally be the discussions that follow.
Have any suggestions?
I am sure a lot of you guys would be experts in the field and would have tonnes of experience in dealing with such problems. Please feel free to leave any suggestion as a comment below. This will help in planning the session accordingly. Looking forward to have a fruitful session with your help!
To be posted here by Thursday EOD.
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