II-PIC 2017: Artificial Intelligence, Machine Learning, And Deep Neural Netwo...Dr. Haxel Consult
Parthiban Srinivasan (VINGYANI, India)
When new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence (AI) and big data. Machine learning is often described as a type of AI where computers learn to do something without being programmed to do it. Deep learning, a subset of machine learning, is proving to work especially well on classification. Big breakthroughs happen when what is suddenly possible meets what is desperately needed. For years, patent analysts have been searching and reviewing terabytes of information, not only patents but also non-patent information. Not only to find prior art but also to identify patents of interest, rate their quality, assess the potential value of patent clusters, and identify potential business partners or infringers. With the rapid increase in the number of patent documents worldwide, demand for their automatic clustering/categorization has grown significantly. Many information science researchers have started to experiment with machine learning tools, but the adoption in the patent information space has been sporadic. In this talk, we aim to review the prevailing machine learning techniques and present several sample implementations by various research groups. We will also discuss how data science compares with machine learning, deep learning, AI, statistics and applied mathematics.
From Lab to Factory: Creating value with dataPeadar Coyle
One of the biggest challenges in Data Science, is deploying Machine Learning models. There are cultural and technological challenges and I'll explain these and share some insights/ solutions.
"How to Master your Data" with Ashish, Data Science Lead at JodelTheFamily
Did you hear? 'Data is the new Oil' ;)
But when should you start thinking about it? What kind of data will be useful? How do yo build a data team and structure it inside an organisation?
These are all questions Ashish Kalra faced when he took the lead of Data Science at Jodel, a platform than enables you to discover and participate in conversations near you.
They now have 1 million users creating more than 6 million posts a day. That's a LOT of data!
Ashish built the data team from scratch and developed a step-by-step framework which he shares with you.
This talk is addressed to founders, data product managers, data scientists and other data enthusiasts!
User Experience 6: Qualitative Methods, Playtesting and InterviewsMarc Miquel
This presentation introduces the most fundamental qualitative methods: the playtesting and the interview. It discusses when to use it and the possible bias the researcher may incur.
These slides were prepared by Dr. Marc Miquel. All the materials used in them are referenced to their authors.
II-PIC 2017: Artificial Intelligence, Machine Learning, And Deep Neural Netwo...Dr. Haxel Consult
Parthiban Srinivasan (VINGYANI, India)
When new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence (AI) and big data. Machine learning is often described as a type of AI where computers learn to do something without being programmed to do it. Deep learning, a subset of machine learning, is proving to work especially well on classification. Big breakthroughs happen when what is suddenly possible meets what is desperately needed. For years, patent analysts have been searching and reviewing terabytes of information, not only patents but also non-patent information. Not only to find prior art but also to identify patents of interest, rate their quality, assess the potential value of patent clusters, and identify potential business partners or infringers. With the rapid increase in the number of patent documents worldwide, demand for their automatic clustering/categorization has grown significantly. Many information science researchers have started to experiment with machine learning tools, but the adoption in the patent information space has been sporadic. In this talk, we aim to review the prevailing machine learning techniques and present several sample implementations by various research groups. We will also discuss how data science compares with machine learning, deep learning, AI, statistics and applied mathematics.
From Lab to Factory: Creating value with dataPeadar Coyle
One of the biggest challenges in Data Science, is deploying Machine Learning models. There are cultural and technological challenges and I'll explain these and share some insights/ solutions.
"How to Master your Data" with Ashish, Data Science Lead at JodelTheFamily
Did you hear? 'Data is the new Oil' ;)
But when should you start thinking about it? What kind of data will be useful? How do yo build a data team and structure it inside an organisation?
These are all questions Ashish Kalra faced when he took the lead of Data Science at Jodel, a platform than enables you to discover and participate in conversations near you.
They now have 1 million users creating more than 6 million posts a day. That's a LOT of data!
Ashish built the data team from scratch and developed a step-by-step framework which he shares with you.
This talk is addressed to founders, data product managers, data scientists and other data enthusiasts!
User Experience 6: Qualitative Methods, Playtesting and InterviewsMarc Miquel
This presentation introduces the most fundamental qualitative methods: the playtesting and the interview. It discusses when to use it and the possible bias the researcher may incur.
These slides were prepared by Dr. Marc Miquel. All the materials used in them are referenced to their authors.
11. April 5, 2010 nath@microsoft.com 11 Brain activity ElectroEncephaloGram(EEG) functional Near InfraRed (fNIR) Cognitive load Aha! moment
12. New perspectives o Insights o Analysis strategies o Cognitive load nath@microsoft.com Many Thanks to Desney Tan, Scott Counts, and Ed Cutrell for their insights on the topic! Challenges Stimuli o Analysis o Validationo