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UID:034a4035-be0b-4ccf-86d9-08eb01152c87@support.access-ci.org
DTSTAMP:20240716T135648Z
DTSTART:20240723T190000Z
DTEND:20240723T220000Z
SUMMARY:Intro to AI & Mini-Hack @ PEARC24
DESCRIPTION:Join ACCESS Support and members of the SCIPE/CIP teams for a sh
 ort Introduction to AI followed by a fun and engaging "Mini-Hack" with pri
 zes and a 3k travel grant award. We hope to see anyone of any level join u
 s for this exciting event—you do not even need to know how to code! This
  event caters to the conceptual level (true beginner with little knowledge
 /experience) up to the more expert level. Students are welcome and encoura
 ged to join us. This "Mini-Hack" project involves an exploration of IMDB 
 movie reviews (50k) for supervised machine learning to predict positive or
  negative sentiment. How do computers “learn”? How do they predict wha
 t movie you would enjoy? What are the internal representations, the “dig
 ital fingerprint” that makes this learning possible?Natural language pro
 cessing is a rapidly evolving field of computer science that uses human la
 nguage as input and/or output and transforms human-authored content, like 
 movie reviews, into a numerical representation appropriate for use with ma
 chine learning algorithms. New representations for text and speech are bei
 ng developed constantly, with increasing sensitivity to context and expres
 sive power.Supervised machine learning makes associations between input fe
 ature representations and their labels. For example, a movie review could 
 be encoded as a list of all the words it contains and training would help 
 to learn weights associated with words, and how much each word contributes
  to a ‘positive’ or ‘negative’ sentiment label.Participants will u
 se Python and sklearn software to:Extract feature representations of text 
 movie reviews, including Bag-of-Words, Averaged Word Vectors, Tf-idf, and 
 N-gramsGenerate word clouds to visualize groups of documents and their cha
 racteristic wordsRun basic machine learning algorithms including Naïve Ba
 yes and Random ForestsLearn about training/test split for conducting machi
 ne learning experimentsEvaluate feature choice and its impact on test resu
 ltsTime permitting, participants will also use more advanced deep learning
  to predict new movies a user is likely to enjoy, given their past viewing
  habits.Registration: Email your name and organization to Alana.Romanella
 @colorado.eduFor updates and event information: Join the AI Mini-Hack Affi
 nity Group 
URL:https://support.access-ci.org/events/7501
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