1st May

Starts From

2 Years




REVA University

Degree awarded by

About the Course

Artificial intelligence has permeated into every sphere of life, bringing smartness and efficiency in all our endeavours. AI today is seen as the panacea and accelerator for solving many of the nagging human problems. REVA Academy for Corporate Excellence and CloudxLab proudly present a unique Masters’ program loaded with experiential learning in a gamified environment with real-time projects solving life problems with AI.

Program Highlights

  • Experiential Learning with Gamified Labs with Auto assessments
  • Opportunity to work with MNCs sponsored Real time projects
  • Alumni Status from REVA University and Reva Academy for Corporate Excellence
  • Globally valid degree with WES recognition and UGC approval

Degree Certificate

India Today


Best Private University

*India Today



Ranked University in Karnataka


QS I-Gauge


Band Rated Indian University

*QS I-Gauge



Ranked University Category of Super Excellence


Hands-on Learning

hands-on lab
  • Gamified Learning Platform

  • Auto-assessment Tests

  • No Installation Required

Faculty Members

Mentor Dr. J B Simha

Dr. J B Simha

Professor and Chief Mentor

AI and CTO of ABIBA Systems

Mentor Dr. Shinu Abhi

Dr. Shinu Abhi

Professor and Director - Corporate Training

REVA University

Mentor Dr. Vaibhav Kumar

Dr. Vaibhav Kumar

Associate Professor - RACE

REVA University

Mentor Praveen Pavithran

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Mentor Ratnakar Pandey

Ratnakar Pandey

Leading ML and Analytics for Customer Service, Amazon

Mentor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Mentor Venkat Karun

Venkat Karun

Staff Software Engineer


Mentor Pradeepta Mishra

Pradeepta Mishra

Lead Technical Architect

AI, L&T Infotech

Mentor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Course Curriculum

First Semester

1. Software Engineering for AI
This course covers the software components required for machine learning. The student will be able to code in python and understand databases both structured and unstructured. This course covers the following contents: -
  • Python, Advantages and Disadvantages of Python, Basics of Python, IDE Overview, Programming Basics-List, Tuples, Sets & Dictionaries, Conditional Statements. Concept of Loops & Functions, List Comprehension, Functions, Object-Oriented Programming
  • NumPy, Pandas, Numpy arrays, Numpy functions, Pandas, Data frame and manipulations, Visualization Libraries, Matplotlib packages, Distribution plots, Scatter plots, Heat maps
  • Introduction to Linux, working with Linux, git introduction
  • SQL Databases, SQL operations, Introduction to NoSQL
2. Mathematics for Machine Learning
This course covers the mathematics needed for Machine Learning. The student will learn mathematics for machine learning using python. This course covers the following contents: -
  • Statistics, mean, mode, median, standard deviation, skews, variance
  • Representing matrices with NumPy, matrix operations, multiplication, inverse operations, solving equations using Gaussian elimination, Vectors, Cross Product, Dot Product, Eigenvalues, Eigen Vectors
  • Calculus, Differentiation, Partial Derivatives, Chain rule, Power Series, Taylor Series, Linearization, Multivariate Taylors, Linear Regression, Least Squares. Newton Raphson, Gradient Descent
  • Combination, Permutations, Probability theory, Bayes Theorem
3. Machine Learning Fundamentals
This course introduces the students to machine learning. This will make the students able to understand the philosophy of machine learning, regression and classification. This course covers the following contents: -
  • Hands-on end to end machine learning example with regression
  • Introduction to Classification, Metrics for Classification, Multi-label, Multiclass classification
  • Training Machine Learning models, Polynomial regression, logistic regressions, regularization
  • Training and Visualising Decision trees, CART training algorithm, GINI
4. Advanced Machine Learning
This course teaches advanced machine learning algorithms. This will make the students able to understand Support Vector Machine, Ensemble Learning, Principal Component Analysis, Unsupervised learning. This course covers the following contents: -
  • Support Vector Machines, Linear SVMs, Non-Linear SVMs, SVM Regression
  • Random Forests, Ensemble Learning, Voting Classifiers, AdaBoost, Gradient Boost, Stacking
  • Understand Dimensionality reduction, Manifold Learning, PCA, Kernel PCA
  • Unsupervised learning, Clustering, K-Means, DB Scan, Gaussian Mixtures
5. Guided Projects
This hands-on Guided Project on Machine Learning will show the students how to solve real-world problems. The following projects will be covered in this course.
  • Forecast bike rentals
  • Finding the group of prospective buyers of new apartments

Second Semester

1. Introduction to Deep Learning
This course will introduce the basics of Deep Learning and the Deep Neural Networks to the students. The students will be able to make custom Deep Neural Networks and train them. This course covers the following contents: -
  • History of Deep Neural Networks
  • Backpropagation
  • Keras
  • Building Deep Neural Networks
  • Training Deep Neural Networks
2. Advanced Deep Learning
This course will introduce the students with the advanced techniques for training and customising the deep neural networks. After this course, the students will be able to make custom Deep Neural Networks and train them. This course covers the following contents: -
  • Advance training techniques for neural networks
  • Understand issues training large Deep Neural Networks
  • Regularization for DNNs
  • Deep Dive into Tensorflow and its lower-level API
  • Custom Deep Learning Models: Writing custom models, implementing custom training
  • Pre-processing large amounts of data for training
3. Computer Vision for Image and Video
In this course, the students will learn how Convolutional Neural Networks (CNNs) achieve superhuman performance on complex visual tasks. After this course, the students will be able to build Neural Networks that do computer Vision and process both images and video. This course covers the following contents: -
  • Introduction to CNNs, Filters, Pooling Layers, Building CNNs
  • Overview of popular classification models, train classification models on a custom dataset
  • Introduction to OpenCV, basic operations with OpenCV, filters, thresholding edge detection, processing videos
  • Object Detection, Single Shot Detectors, YOLO, Training YOLO on a custom dataset
4. Sequence Modelling
In this course, the students will learn how to use Neural Networks to predict future data using older data. After this course, the students will learn to model time series data with Neural Networks and predict future values. This course covers the following contents: -
  • Recurrent Neural Networks, Memory cells, Sequences, Training RNNs
  • Forecasting a time series
  • Simple RNNs
  • Deep RNNs
  • Long Sequences
  • Unstable Gradients
5. Natural Language Processing
In this course, the students will learn the Natural Language Processing from its basics to advanced implementation. They will learn to use Deep Neural Networks for Natural Language Processing and Sentiment Analysis. This course covers the following contents: -
  • Generating Shakespearean text using RNNs
  • Sentiment Analysis
  • Encoder-Decoder Network for Neural Machine Transfer
  • BEAM Search
  • Attention Mechanisms

Third Semester

1. Generative Adversarial Networks
This course introduces the students with Autoencoders and GANs. In this course, the students will learn about artificial neural networks capable of learning dense representations of input data without any supervision. This course covers the following contents: -
  • Data representations
  • Linear Autoencoders
  • Stacked Autoencoders
  • Convolution Autoencoders
  • Recurrent Autoencoders
  • Generative Adversarial Networks
  • Training GANs
  • Deep Convolution GANs
  • Style GANs
2. Reinforcement Learning
In this course, the students will learn to apply the power of Deep Neural Networks to Reinforcement Learning. The students will learn about artificial neural networks can be used to make powerful reinforcement learning applications. This course covers the following contents: -
  • Optimizing Rewards
  • Policy Search
  • Open AI Gym
  • Markov Decision Processes
  • Q-Learning
  • Deep-Q Learning
  • TF-Agents
  • Curiosity Based Learning
  • Difference between Curiosity Based Learning and Reinforcement Learning
3. Production and Maintenance of an AI system
This course introduces the students with different methodologies to deploy AI systems. The students will learn how to deploy AI systems on the cloud and embedded device. This course covers the following contents: -
  • Serving a TensorFlow model on a cloud, TensorFlow serving, create and use a prediction system on the cloud
  • Creating lite models, deploy on embedded devices
  • Models with GPUs, Colab
  • Training models across multiple devices, model parallelism, data parallelism, distribute strategies for training, training with Tensorflow cluster
4. Recommendation Engine
This course teaches how to build a recommendation engine with machine learning. It also gives an exposure to building a recommendation engine. This course covers the following contents: -
  • Introduction to Spark MLLib
  • Collaborative filtering algorithm
  • Build recommendation engine in Spark MLlib using Alternating Least Square algorithm
5. Guided Projects
In this module, the students will work on guided projects where they will get the hands-on exposure to working real-world implementation of advanced deep learning techniques. The students will get an opportunity to work on the following projects: -
  • Neural style transfer project
  • Deploy an AI system on the cloud

Fourth Semester

Dissertation Project
The program participants will take any research-oriented high-quality project of their choice that will end up with a live application, project report and a research paper.
Download Curriculum


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Application Process

  1. Step 1. Submit the application form and SOP(Statement of Purpose)
    Register by filling the application form
  2. Step 2. Admission Test
    Online Admission Test will be conducted on CloudxLab
  3. Step 3. Personal Interview
    Personal Interview will be taken based on test scores and SOP reviews
  4. Step 4. Join The Program
    If selected, the admission office will send the letter of acceptance. Submit the fees in due time to confirm the seat

Scholarship Details

  1. For Everyone
    Up to USD 410 based on SOP, test result and your academic performance.
  2. For Women in STEM
    Up to USD 1,100 scholarship. Only 5 seats are available on first come first serve basis.


Year 1
  1. 1st Installment - USD 2,500 (Before May 1, 2021)
  2. 2nd Installment - USD 2,500 (Before Sep 1, 2021)
  3. 3rd Installment - USD 2,000 (Before Jan 1, 2022)

Year 2
  • 4th Installment - USD 1,800 (Before May 1, 2022)

Please note that there is an additional 5% off for one-time payment


  • Batch Starts on May 1, 2021
  • Duration of 2 Years
  • Degree awarded by REVA University, Bengaluru
Apply Now »


Frequently Asked Questions

Why do I learn M.Sc. in Deep Learning and Artificial Intelligence of REVA Academy for Corporate Excellence in association with CloudxLab?

In recent years, Deep learning and artificial intelligence domains are showing rapid growth. The cornerstone of the two-year Master’s program in Deep Learning and Artificial Intelligence is the sponsored projects in association with organizations that have set new benchmarks in AI implementation. This specialization is designed for those who want to gain hands-on experience in solving real-life problems using big data, machine learning and deep learning. Hence, enrolling to M.Sc. in Deep Learning and Artificial Intelligence program of REVA Academy for Corporate Excellence, REVA University in association with CloudxLab will take your career to the next level.

Who can apply to M.Sc. in Deep Learning and Artificial Intelligence program?

B. Tech/BE in any engineering stream, BCA/B.Sc. in IT/CS/Electronics, B.Com/BBA/MBA with Computer Applications (must have studied Mathematics or Statistics at +2 level), BA in Economics with Mathematics or Statistics as one of the subjects with 50% marks (45% in case of candidates belonging to SC/ST) in aggregate from any recognized university/institution and 2 years of relevant work experience are eligible to apply.

What is the selection criteria for the program?

Our selection process for the program includes entrance exam and personal interview. Only qualified candidates are permitted to take the entrance exam and attend the subsequent personal interview.

What are the subjects that I study under M.Sc. in Deep Learning and Artificial Intelligence?

You will be learning Software Engineering for AI, Deep Learning, Fundamentals, and Advanced Machine Learning, Natural Language Processing, Sequence Modeling, Generative Adversarial Networks, Reinforcement Learning, Recommendation Engine, Computer Vision, etc., during the program

What about the course delivery?

M.Sc. in Deep Learning and Artificial Intelligence in association with CloudxLab is offered in a hybrid format, i.e. both online and offline (on-campus) learning.20 hours per semester will be offline classes

What is the demand for deep learning and artificial intelligence professionals?

The demand for artificial intelligence and deep learning professionals is ever-growing in this technology-driven world. Talented and skilled individuals can change the world by applying AI applications in various domains.

I am not from India. Can I enroll in this course?"

The right to admission is subject to the final document certification by the University as per UGC regulations

What is the duration of this program?

The duration of the M.Sc. in Deep Learning and Artificial Intelligence program is 2 years, which consists of 4 semesters.

Can I pay my fees in installments?

Yes, it is possible to pay the fees in installments using your convenient payment modes such as online bank transfer, credit/debit card transfer, cheque, Paytm transfer, etc. In the first year, you have to pay 3.8 lakhs in 3 installments of 1.3 lakhs and second year fee will be one lakh. Please note that women candidates with STEM background will get a special discount on the total fee.

When do you start the admissions for new batches?

According to the academic calendar, admissions to M.Sc. in Deep Learning and Artificial Intelligence will start in April 2021.

Can I apply for an education loan?

Bank Loans are available from ICICI Bank, Axis Bank, and HDFC Bank. Please find details here: http://bit.ly/2Z9dazN

Can I apply for the program if I don’t have any experience in IT or artificial intelligence domain?

No. If you do not have 2 years of experience in IT/Artificial intelligence domain, then you are not eligible for our artificial intelligence program.

Is the M. Sc. in Deep Learning and Artificial intelligence program accredited?

Yes, it is. The M.Sc. in Deep Learning and Artificial Intelligence program is approved by UGC.

What if I apply after the batch starts?

RACE or CloudxLab cannot guarantee admission after the application deadline, even though you can contact us for admissions anytime. Once we admit 60 students, we will close the admission for that batch. When the admissions are closed for a particular batch, you can apply only for the next batch. Hence, it is highly recommended to complete the application process as early as possible.

How do you promote experiential learning in M.Sc. in Deep Learning and Artificial intelligence?

CloudxLab offers a gamified environment where the learners receive a constructive and fun-filled learning environment. By integrating creative design to intuitive apps, CloudxLab creates a seamless and sustainable experiential learning methodology for the learners to succeed in their domain. After finishing this specialization, you will find creative ways to apply your learning to your work like building a robot that can recognize faces or change the path after discovering obstacles on its path.

What if I discontinue the program after completing first year (two semesters)?

A candidate admitted to M.Sc. in Deep Learning and Artificial Intelligence can exercise an option to exit with P.G Diploma in Deep Learning and Artificial Intelligence after earning 54 credits successfully as specified in the Scheme of Instruction. However, such candidates can directly enter into the third semester (2nd year) as per the regulations.

Do you provide learning materials to the participants?

Yes, once you’ve joined the program, you will get the login details to access the Learning Management System (LMS), where you can find all the learning materials such as pre-readings, assignments, in-class resources, and recorded videos of classroom sessions, additional reading materials, webinars, and other learning resources added as per the requirements.

How do you perform the evaluation process?

You have to go through a continuous evaluation process. The candidates will be assessed based on their performance in MCQs, Viva and online/offline tests. The tests consist of objective, programming and subjective questions. All subjects will have a proctored online test at the end of that course. The pass criteria will require going through all recorded materials, completion of assignments and guided projects.

What kind of outcomes can I expect after the completion of my program?

Upon the completion, you will master relevant tools, industry skills, and industry best practices. Our program enables you to achieve professional and leadership goals.

Can I expect any placement support?

Yes, we do offer placement assistance that includes career guidance, resume building tips and mock interviews. Each participant will receive staunch support from the industry mentors, who also direct you through various placement opportunities within the industry. Above all, we are partnered with leading MNC’s that offer placement opportunities to our participants.