Learn Convolutional Neural Networks, Free

At Univ.AI, our vision is to take state-of-the-art education beyond the confines of the world’s top institutions. We are offering up to 40 talented applicants, an opportunity to deep dive into convolutional neural networks and learn how to implement transfer learning in neural networks. You will learn LIVE from Harvard faculty, Dr. Pavlos Protopapas over a 6-week period, absolutely FREE.

The program is accessible to anyone around the world with a computer and an internet connection. During the course of six weeks, Dr. Protopapas will take all lectures over live zoom (videoconference), and our group of talented mentors and teaching assistants will conduct sessions, also over live zoom (videoconference), to assist you with doubts and questions you may have. The course will end with a weeklong project for you to apply your newly learned skills. You will have access to all lectures via recorded video, in case live session timings are unsuitable for you.

The program will begin on November 3, 2020.

Apply Now

Program Highlights

Course Dates: November 3 - December 13, 2020 (6 Weeks)

Where: Anywhere with an internet connection. Live, Via Zoom. Recorded video available.

Live Session Timings: 

Lecture Sessions: - Wednesday Series: 9:00 PM - 10:30 PM - Saturday Series: 7:30 PM - 9:00 PM. Timings are in IST

Office hours on Monday

Seats: up to 40

Program Fee: INR 0 for the selected students (up to 40), no application charges

Application Deadline: [UPDATE] October 28, 2020

Who can apply?

Students and working professionals from anywhere in the world who want to advance their AI and ML learning journeys. You will need to invest about 10-12 hours per week, all-inclusive.

What are the course prerequisites?

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

How to apply?

Please hit Apply after reading the Terms and Conditions towards the end of this page, and follow instructions. You will be asked to fill up a questionnaire, and take a test. Please answer the questions in detail, to the best of your knowledge. We will select up to 40 most qualified applicants for this program. 

Who will be selected?

Applicants who qualify based on their application and test will be admitted on a rolling basis. We will close admissions as soon as we reach 40 qualified applicants. However, if you want to take the program anyway and you meet the pre-requisites, please head over to the paid program to learn convolutional neural networks.

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The Faculty

Dr. Pavlos Protopapas, Professor, Harvard University | Lead Faculty, Univ.AI

Watch: Dr. Protopapas teaches Artificial Intelligence

00:18 Introduction & Class Pre-requisites

04:21 The learning platform

05:59 Lesson 1 - Part A demo

19:51 Breakout room

21:15 Live Q&A

What to expect from the program? 

With this course, you will advance your data science learning journey and learn about Convolutional Neural Networks and how they’re being used for machine learning. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

The objective of this module is to provide you, with a deeper intuition and understanding of neural networks including network architecture choices, activation functions, feed-forward, convolutional neural networks and auto-encoders.

At the end of this course, you will be able to run a variety of advanced machine learning models, and learn to apply them to practical image recognition problems. 

The program will include:

  • Twice-weekly live online classes
  • TA/Mentor Sessions
  • End-of-module project

Program Calendar

Applications close: October 20, 2020

Terms & Conditions

  • Admissions to the free program are limited to up to 40 seats and are at the discretion of the Univ.AI admissions committee, upon successful completion of the application and the test by the candidate
  • The Univ.AI admissions committee reserves the right to revoke admission in case any information provided by the applicant is found to be false at any time during the course of the program
  • Students can only take only one program free with Univ.AI. If you have taken any of our free programs before, you are ineligible to apply for this program.

Accept & Apply