Computer Science and AI
108 Artificial Intelligence: Theory, Methods, and Application
This workshop is for students interested in careers or degree programs that focus on Computer Science, Software Engineering, or Artificial Intelligence and would like to get a taste of some of the exciting and impactful work being done in this field. Some basic familiarity with PYTHON, algebra 2. Essential calculus is helpful but not required.
In this workshop, you will learn about the field of Artificial Intelligence and focus on understanding how we can teach machines to see (Computer Vision), understand natural language (Natural Language Processing), plan and interact with the world and their environment (Reinforcement Learning), and finally bring it all together by focusing on an application that combines these areas (Robotics). From self-driving cars to virtual assistants, artificial intelligence is used worldwide in a variety of exciting and creative ways. In the first portion of this workshop, we will focus on understanding the state-of-the-art methods in some of these various subfields and looking at some open questions and potential future applications. We will also develop some basic Python programming skills and learn how to implement some natural artificial intelligence with just a few lines of code. In the second portion of the workshop, each student will pick a specific question such as, “How do self-driving cars detect obstacles around them?” or “How does Siri make sense of the sentences I say to her?” and work to answer them by comparing existing methods. Students will also attempt to propose new solutions and speculate about the potential weaknesses of existing ones.
【Sample research topics】
How Do NLP chatbots respond with the appropriate emotional affect
Reinforcement learning in Finance
How effective is the current state-of-the-art lip to speech synthesis?
What kinds of applications of reinforcement learning in different games?
An Experimental Analysis on mBART: A Sequence-to-Sequence
Denoising Auto-encoder Neural Machine Translation Model
Machine Learning in Heart Arrhythmia Detection