AI-2: Convolutional Neural Networks

Standalone Course

This 6-week module is our second course in AI, and is designed to teach you the basics of convolutional neural networks.

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November 3, 2020
To be decided


6 Weeks





(Interest free EMI available)

Registration closes

October 28, 2020
To be decided

Program overview

This module will take you from refreshing the basics of AI/ML from AI-1 to topics such as convolutional neural networks, auto-encoders and generative adversarial networks. It starts with an introduction of neural networks and it covers the key concepts such as feed forward neural networks, backpropagation, regularization and optimization. Further along the course you will deep dive into convolutional neural networks and learn how to implement transfer learning in neural networks and end with auto-encoders and generative adversarial networks.

Who should apply

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Course outcomes

At the end of this course you will be able to:

  1. Implement and deploy image recognition and object classification.
  2. Have a deep understanding of machine learning and deep learning, and thus be able to read papers, and self-learn more advanced tasks and topics in machine learning and deep learning.

Topics covered

  • Introduction, Review of  Feed Forward Network Networks (Network Architecture and Design Choices)
  • Backpropagation and Optimizers
  • Regularization for Neural Networks, Early Stopping, Data Augmentation, Dropout, and Batch Normalization
  • Convolutional Neural Networks: Basic Concepts and Architectures
  • Convolutional Neural Networks: Receptive Fields, Strides and Saliency Maps
  • Convolutional Neural Networks: State of the Art CNNs
  • Neural Net Transfer Learning
  • Auto-encoders
  • Generative Adversarial Networks

Program Structure

Live online classes
1. Live Online Classes

Bi-weekly lessons with labs and quizzes

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 learned

4. Capstone

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


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Not ready to enroll but want to learn more? Download the certificate brochure to review program details.

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Financial Assistance

Interest-free EMI

Interest-free EMIs are available for everyone. Apply easily through our banking partner once you complete the application process.

Scholarships for the best

Univ.AI Scholarships are available for the most promising candidates, based on performance in our application test, and your financial needs. Our scholarships will pay part of your tuition fee.

Income Sharing Agreement

Pay a sum equivalent to 30% your course fee at the start. After course completion, pay in instalments of 15% of your monthly salary for a period of 24 months to be free of any financial obligation to us. You payments are capped at the course fee (not including the sum paid upfront), or 24 months from your first payment, whichever happens earlier. Speak to a counsellor to determine eligibility.

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Frequently asked questions

Who should take stand-alone modules?

If you cannot devote the time necessary to complete an entire program as per schedule and want to do it module by module. Or, if you need only some part of the curriculum, then you can only take classes for your personal learning objectives.

I have just taken AI-1 and AI-2. Can I directly join a capstone project or apply for a practice internship?

Project programs and Practice Internship Programs will be announced soon for candidates who have already completed requirements for them. You can apply for many of them if you have proficiency in the material taught in AI-1 and AI-2.

Can I take Univ.AI’s Advanced AI Programs after I complete AI-1 and AI-2?

Yes, you can. And you will also be eligible for direct admission without entrance exam to our Advanced AI Program.

Do I need to apply and take an entrance exam to take each module separately?

No, you do not. You can select a module and go straight to payment.

Do you advise taking stand-alone modules or complete programs?

By the virtue of their long-term association with the system, program students get better opportunities for company sponsored projects, paid consulting projects, research opportunities with top faculty around the world and more. If possible, we recommend that you take at least two modules one after the other, and then take a break. That will enable the system to help you better.

Is the financial assistance/scholarships available for individual modules?

No. However, do contact if you wish to participate and are unable to for reasons of financial hardship. Please be prepared to submit evidence of financial hardship if you wish to considered for hardship-based grants.

What are the pre-requisites for this course?

You are expected to have programming experience at the level of Harvard’s CS50, statistics knowledge at the level of Harvard’s Stat 110 or above and basic machine learning concepts such as model fitting, test-validation, regularization, etc.

Programming Experience:

  • Experience with Python: functions, Basic data structures, variable scope, modules, NumPy, SciPy, Matplotlib

Statistics Experience:

  • Basics of probability, conditional probability, Bayes’ theorem
  • Normal and Gaussian distribution
  • Central Limit Theorem
  • Differentiation and partial derivatives

Machine Learning Experience:

  • Basic understanding of supervised and unsupervised learning
  • Regression and Classification
  • Loss functions
  • Overfitting and Regularization
  • Model Selection
What will be the course timings?

Lecture Sessions:
Wednesday Series: 9:00 PM - 10:30 PM - Saturday Series: 7:30 PM - 9:00 PM

Lab Sessions:

  • Thursday Series: 9:00 PM - 10:00 PM
  • Sunday Series: 7:30 PM - 8:30 PM

Office hours:

  • Mondays: 8:00 PM - 9:00 PM

All timings are in IST

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