Speaker "Aakriti Srikanth" Details Back



The most optimal machine learning algorithms


This session will help in selecting the most optimal machine learning algorithm for the particular task in hand. The session will touch upon Classification algorithms such as Logistic Regression, Support Vector Machines(SVM), Random Forest(RF) and Naives Bayes algorithms. These algorithms utilize supervised learning or label data sets for training thereby determining the right category. We will also touch upon algorithms such as Linear Regression for continuous valued functions, Collaborative filtering or Recommender functions such as Alternative Least Squares, clustering algorithms such as k-means and dimensionality reduction algorithms. Ever thought of which type of algorithm Netflix uses to make movie recommendations or Amazon uses to make product recommendations. Come find all of your answers here. Video of speaker for brief overview - Link to Aakriti’s AI video:


Aakriti Srikanth( is currently on the board of Newfound Technologies, hails from the Kellogg School of Management & the Ohio State University and the organizer of the  Boston Data Science meetup (has about 800+ members including notable data scientists from Google, Facebook, Spotify, Amazon, IBM Watson and others). She won the IBM Watson competition and was a national finalist back when Watson first won against the grand champion on the Jeopardy game. She has been involved in many of the Artificial Intelligence conferences(the AI Summit SF, MLConf, BankAI, AI World, AI Summit NYC, GraceHopper conference).  With a Masters in Computer Science(specialization in AI) and as a woman in technology leader(Red Hat & formerly IBM Watson),  she likes mentoring upcoming stars in AI(particularly women in AI).