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AI-4: Generative Models & Reinforcement Learning

This sprint will provide you with the fundamental understanding of the concepts behind Generative Models and Reinforcement Learning and how to apply them to real world problems. 


Split into 2 parts, AI-4 starts with a review of Network Building Blocks, followed by an overview of Generative Adversarial Networks and their applications. It also touches on Latent Space Interpretation. The second half of this sprint introduces you to the field of Reinforcement Learning. At the end of the sprint, you will be able to build efficient generative models, and work with reinforcement learning problems. 

AI-4 will prepare you for sought after job opportunities in AI and also equip you to conduct original research in your area of interest.

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Applications opening soon
Registration Closed

Starts

June 7, 2021
To be decided
Self-paced

Duration

6 weeks

Time Investment

10 to 15 hours per week
Online

Format

Live Online
Online

Fee

Introductory Price: $750
List Price: $1,250

₹40,000

Registration closes

June 6, 2021
To be decided

Course Outcome

Program Overview

Program Overview

After successfully completing the program, you will be:

  • Able to define Generative Adversarial Networks suitable to solve the task at hand.
  • Able to understand the concept of Reinforcement learning.
  • Able to define your own agent and environment system to solve various problems.
  • Awarded an advanced certification in AI. 
  • Eligible to be a part of the Capstone internship program.
  • Prepared to pursue advanced (graduate, doctorate) level studies in AI.
  • Qualified to seek upper-intermediate and senior level AI positions in the industry.

After successfully completing the program, you will be:

  • Able to define Generative Adversarial Networks suitable to solve the task at hand.
  • Able to understand the concept of Reinforcement learning.
  • Able to define your own agent and environment system to solve various problems.
  • Awarded an advanced certification in AI. 
  • Eligible to be a part of the Capstone internship program.
  • Prepared to pursue advanced (graduate, doctorate) level studies in AI.
  • Qualified to seek upper-intermediate and senior level AI positions in the industry.

Who should apply

Learners and practitioners who have an understanding of intermediate AI concepts, including Convolutional Neural Networks and Transfer Models, and are looking to master more advanced concepts should enrol for AI-4. You are required to have a strong foundation in Statistics, Computer Science & Mathematics.

See if you are ready

Prerequisites

To take this sprint, you should have knowledge of beginning and intermediate AI as exemplified by the topics covered in AI Basics. Additionally, a strong knowledge of Python and high-level Machine Learning Libraries such as Tensorflow and Keras is required to enrol for AI-4.


Class Timings

Classes are held twice a week in the mornings US time : 9:30 am-11:30 am EST

In addition there are 1-2 labs a week, and 1-2 office hours during the same times but different days of the week. 

Exact details vary slightly by course, depending on the teaching team. Please refer to course calendar for each course.


Classes are held twice a week from 9:30 AM - 11:30 AM Eastern Standard Time

In addition, there are 1-2 labs a week, and 1-2 office hour sessions all during the same time bracket. 

Exact details may vary slightly by course, depending on the teaching team.

Course Structure

Live online classes
1. Live Online Classes

Bi-weekly live, cohort-based lessons with labs and quizzes

Mentorship
2. Mentor-Supported

Ten or more hours/week to interact with an experienced & accomplished mentor

Module end Project
3. End-of-Module Project

Complex problems that challenge you to apply what you learn

Capstone
4. Capstone

10–12 week long Capstone Project with one of our partner companies or faculty

Program Faculty

Dr Pavlos Protopapas

Founding faculty Univ.AI

Pavlos is the Scientific Program Director, Institute for Applied Computational Science (IACS) at Harvard University, and leads the data science masters program at Harvard.

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Dr Raghu Meka

Founding faculty Univ.AI

Raghu Meka is an Associate Professor of Computer Science at UCLA. He is interested in complexity theory, learning and probability theory. He got his PhD at UT Austin under the (wise) guidance of David Zuckerman. He was at Princeton as a postdoctoral fellow at the Institute for Advanced Study with Avi Wigderson, and at DIMACS, at Rutgers. Prior to UCLA, Raghu was at the Microsoft Research Silicon Valley Lab. Raghu is the recipient of an NSF Career Award.

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Dr Achuta Kadambi

Founding faculty Univ.AI

Achuta is an Assistant Professor at UCLA where he directs the Visual Machines Group. Powered by artificial intelligence, the group creates imaging systems that can see the unseen. Achuta received his PhD from MIT, his BS from UC Berkeley. Earlier, Achuta was named to the Forbes 30 under 30 list of inventors, and received the NSF research initiation award. Most recently Achuta won a Google Faculty Award.

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Topics Covered

AI 4

GANs
Markov Decision Processes
Deep Q Networks
Policy Gradient Methods
Actor Critic models
Model based methods

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