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AI Mini-Hack

Tuesday July 23, 2024
PEARC, Providence, RI
3-6 PM
Registration: Email your name and organization to Alana.Romanella [at] colorado.edu (alana[dot]romanella[at]colorado[dot]edu) (Alana Romanella)

Join ACCESS Support and members of the SCIPE/CIP teams for a short Introduction to AI followed by a fun and engaging "Mini-Hack" with prizes and a 3k travel grant award. We hope to see anyone of any level join us 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 encouraged 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 what movie you would enjoy? What are the internal representations, the “digital fingerprint” that makes this learning possible?

Natural language processing is a rapidly evolving field of computer science that uses human language as input and/or output and transforms human-authored content, like movie reviews, into a numerical representation appropriate for use with machine learning algorithms. New representations for text and speech are being developed constantly, with increasing sensitivity to context and expressive power.

Supervised machine learning makes associations between input feature 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 use Python and sklearn software to:

  • Extract feature representations of text movie reviews, including Bag-of-Words, Averaged Word Vectors, Tf-idf, and N-grams
  • Generate word clouds to visualize groups of documents and their characteristic words
  • Run basic machine learning algorithms including Naïve Bayes and Random Forests
  • Learn about training/test split for conducting machine learning experiments
  • Evaluate feature choice and its impact on test results

Time permitting, participants will also use more advanced deep learning to predict new movies a user is likely to enjoy, given their past viewing habits.

To register: Email your name and organization to Alana.Romanella [at] colorado.edu (alana[dot]romanella[at]colorado[dot]edu) (Alana Romanella)

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