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Course Instructor

Instructor Bio

Artificial Intelligence & Machine Learning: An Introduction

By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.

Eliezer Yudkowsky, Co-Founder, Machine Intelligence Research Institute

Date(s):

May 7-28, 2023

AI_ML Class
AI_ML Class

Gifted Summer Program May 2022

press to zoom
AI_ML Class
AI_ML Class

Gifted Summer Program May 2022

press to zoom
AI_ML Class
AI_ML Class

Gifted Summer Program May 2022

press to zoom
AI_ML Class
AI_ML Class

Gifted Summer Program May 2022

press to zoom
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Artificial Intelligence (AI) and Machine Learning (ML) are changing the world, and there is going to be an increasing demand for the people who can understand and build AI/ML systems.


All aspects of our life are being digitized and data is being captured, and much of this data is available to anybody who wants to do something with it. More and more of our day-to-day activities will be controlled by or influenced by algorithms. Which means that the most exciting technologies today (and the ones that are also the most successful) are the ones that can make sense of this data in a way humans can. Every company, from Google to Facebook to Netflix to Amazon to Flipkart are turning to AI/ML for best results.


This course will give students a hands-on introduction to Artificial Intelligence and Machine learning. The course will introduce the students to 3 different aspects of AI theory and

practice:

  • Understanding the theory of AI/ML

  • Learning how to build simple machine learning systems using low-code/no-code tools

  • Using AI algorithms with some existing data-sets to solve problems and build games

Specifically, the course will cover the following:


High-Level Concepts

  • What are Artificial Intelligence and Machine Learning?

  • How is it different from normal software and computers?

  • An overview of the most important different approaches to AI

  • What types of problems they're best suited for?

  • And, what are some current applications that they're being used for?

  • The weaknesses of AI

  • The problems caused by inappropriate use of AI

Theory

  • Introduction to the mathematics that underlies ML algorithms

  • Understanding the train-test-validation cycle

  • Understanding the concept of hyperparameters and tuning

  • Understanding overfitting, underfitting, and the bias vs. variance trade-off

  • Techniques for improving accuracy

Building

  • Image/audio/video/text classification using ML

  • Build simple games using ML and Scratch

  • Analyzing some real-world datasets using Python

  • Final Course Project