Convoluted Neural Networks

In this course, you will continue your data science journey to learn more 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, the student, with a deeper intuition and understanding of neural networks including network architecture choices, activation functions, feed-forward, convolutional neural networks and auto-encoders.

5 weeks
Live Online
course fee
Next Cohort
May 14, 2023
To be decided
Classroom based

You learn with a group - your cohort. Build lifelong relationships and learn from one another.

LIVE, from the very best

We seek to recreate the learning environment of elite learning institutions, specifically - the Harvard M.S. in Data Science & AI

Intensive mentoring

Help is always on hand, always live, and more of it than anywhere else in the world

Meet your teacher

Our teachers hail from some of the world’s leading institutions and research labs in AI & Data Science.
In numbers


Contact Hours


Hrs of classes


Hrs of guided labs


faculty office hours


Group projects


Neural Networks: Perceptron and MLP
Neural Networks: Anatomy of Neural Networks and Design Choices
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Neural Networks: Backpropagation and Optimizers
Neural Networks: Regularization for Neural Networks: Dropout, and Batch-Normalization
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Convolutional Neural Networks: Basic Concepts, Kernels, Strides, and Padding
Convolutional Neural Networks: Pooling and CNN Architecture
Convolutional Neural Networks: Backpropagation in Max Pooling, Receptive Fields
Visualization: Feature Map and Saliency Maps
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Transfer Learning + State-of-the-Art Networks
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Who is it for?

Anyone who seeks to learn machine learning and who has the necessary prerequisites. The course is however tailored to students as well as working professionals, who have some background in maths as well as programming.


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, variable scope, classes, modules, NumPy, SciPy, Matplotlib
  • Basic data structures
  • File I/O

Statistics Experience:

  • Basics of probability, conditional probability, Bayes’ theorem
  • Univariate distributions, normal, binomial, Poisson distributions
  • Multivariate normal distribution
  • Central Limit Theorem
  • Machine Learning Experience:
  • Basic understanding of supervised and unsupervised learning
  • Regression and Classification
  • Loss functions
  • Overfitting and Regularization
  • Model Selection

How it works


We have very carefully designed the coursework to give you, the student, a wholesome learning experience. We will hold two weekend sessions per week for a total of five weeks.


Before the session begins, students are expected to complete a pre-class reading assignment and attempt a quiz.

During Session

During the session, we will have live instruction interspaced with collaborative coding in small groups assisted by our teaching assistants. This will help you develop intuition for the core concepts and provide guidance on technical details.


After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class.

session structure


A lab is a TA driven 1.5 hour session that is divided into 3 major parts

Each lab begins by revisiting the quizzes and exercises done in the previous lecture session.After discussing exercises, we will have a semi-formal Q/A session.

All doubts pertaining, but not limited, to the previous session, and homeworks are welcome. The last part of the labs deals predominantly with the upcoming homeworks. It is directed towards giving a brief overview of the homework problem. We will discuss some code to help you get started.

Lab structure


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.

Job Skills

After successfully completing the program, you will be:

  1. Ready to run complex analyses on all kinds of data on your own.
  2. Awarded the Professional Certification in Machine Learning on successful completion of the course.
  3. Prepared to pursue advanced (graduate and above) level studies in machine learning.
  4. Qualified to seek entry level and intermediate machine learning positions in the industry.

Student Projects

Sample class

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We believe in the idea of active learning and our course is designed with the expectation of active participation from the students. Please find a demo of our course style and pedagogy.

Meet our Students & Alumni

Meet our Students & Alumni

Our early Alumni are going places

Frequently asked questions

Do I get a certificate for completing each course?
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For each course you complete successfully, you get a course completion certificate and a grade. All certificates are granted by Univ.AI, and signed by our founding faculty, who are Harvard and UCLA professors.

Do I get college credits for taking Univ.AI courses?
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No, we do not offer college credits for our courses. However, if you choose to pursue further education, based on knowledge acquired at Univ.AI, you will be able to test out of required courses at many universities around the world.

How are students graded and evaluated?
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We conduct both group and individual evaluations. Evaluations are based on your class performance (exercises and quizzes), labs, homeworks, and projects. We ask that you always participate and attempt exercises even if you get them wrong: participation counts towards your grade. 

Detailed evaluation criteria may vary from course to course, and are published prior to each course. Below is a representative grading structure:

  • Quiz & Exercises: 25%
  • Homework assignments: 35%
  • Participation: 10%
  • ~Forums: 5%
  • ~Lectures: 5%
  • Course-end Project: 20%

How do you mentor students at Univ.AI?
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The frequency of live sessions ensures that help is always on hand. 

Highly trained Teaching Assistants (TAs), serve as course mentors. RIght from the in-class exercise during professors’ lectures, to labs, office hours and discussion forums. TAs mentor each step of your learning journey - LIVE. Typically, they will be alongside you, live, with other members on a shared screen, discussing questions, helping clarify concepts and when necessary, typing in lines of code. TAs also grade your work. 

This real-time, live mentoring by TAs makes a decisive and unparalleled difference to the learning experience at Univ.AI.

How much time will I need to commit each week to do well at Univ.AI?
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Programs at Univ.AI are run one-course-at-a-time, and require about 12-15 hours of your time each week, including live classes, labs, office hours, homeworks, and projects. 

Every week, you have two classes of about 90 minutes each

Each session lasts about 90 minutes. 

If you can commit that kind of time every week to learn a new field, then we welcome you to apply for Univ.AI programs in Data Science, and AI irrespective of your stage of career.

What happens if I miss a session?
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We make session recordings available on video shortly after they are completed. Most participants tend to view the lecture video and then follow that up with a live Lab and office hours to catch-up. 

TAs often stay after labs and office hours to answer questions. There are lots of opportunities to catch up with missed sessions.

Enquire Now

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Thank you for your interest!
You can now sign-up for counselling sessions to discuss courses, tuition options, career prospects and more. We will be happy to guide you through your Data Science journey.


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