Applied machine learning for decision making in real-life problems
Machine learning has been a big shot in the last few years. In this talk I just want to highlight two examples (spam and fraud detections) where we can use machine learning to solve the decision making in real-life problems.
The first one is how to use machine learning to detect spam traffics from huge data set (e.g., 1M records/second). The data size, however, is not only the main challenge but also the ambitious spam definition as well as quality of training data (if you can collect). The second one is focus on a challenge where your training data is not only low quality but also extremely unbalanced.
Dr. Le Sy Quang – Google (UK)
Quang Sy Le got Ph.D degree from Japan Advanced Institute of Science and Technology (JAIST, Japan). He has more than ten years working in different academia projects including e.g., Molecular Evolution, 1000 Genome Projects. He is now moving to Google (England) to work on machine learning projects for large data sets.