[MINI] Parallel Algorithms

In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control. From fake reviews and shilling attacks to explainable recommendations and user-selected algorithms, we look at wha ...  Show more

In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, ...  Show more

Machine learning algorithms need examples of data from which they can learn, especially supervised machine learning algorithms. However, one big challenge for those looking to put machine learning into practice is the lack of a sufficient quantity of good quality data examples fr ...  Show more

What does Big Data actually mean? How has the science of Big Data changed recently? What are the potential benefits and pitfalls of Big Data? See omnystudio.com/listener for privacy information. 

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Python has beome the de facto language for working with data. That has brought with it a number of challenges having to do with the speed and scalability of working with large volumes of information.There have been many ...

In the 1950s and 60s - even leading into the 1990s - the cost of storage was so high, that using a 2-digit field for dates in a software instead of 4-digits could save an organization between $1.2-$2 Million dollars per GB of data. From this perspective, programming computers in ...  Show more