Frequently Asked Questions

Most Frequently Asked Questions

You will need the following infrastructure to access our platform:

  1. Device: A Desktop/Laptop/Tablet/Smartphone with camera and mic.
  2. Internet: A regular broadband/wifi connection or a mobile 4G connection

Note: Proctored online assessments can be taken only on a Desktop/Laptop with a web camera and mic (not allowed on tablets/smartphones).

Groups can conveniently connect among themselves at an appointed time for group work, outside the online classes. This feature comes in handy during group activities and projects.
Students can also use the platform 24 x 7 to engage and learn from their peers using our interactive discussion forums available under each course of the programme.
Our platform also has the provision of group-specific discussion forums for offline discussion among the group members.

Our recorded videos have an AI-generated Table of Content (TOC) to enable easy and smart navigation within the videos. Students can click on the items under TOC to directly jump to the relevant section in a video. Video archives also have a Phrase Cloud of keywords/phrases used in the video. Students can click on a particular keyword/phrase to find yellow markers, indicating the points where the selected keyword/phrase was covered on the video timeline.

Selection Process

IISc conducts the selection process for the programme. Candidates are selected based on education, work experience and a statement of purpose submitted along with the application.

Education:  Graduation (BE, Btech, BSc, MSc etc) or Masters in Science | Engineering | Management (relevant)

Experience: Working professionals (with minimum 1 year of work experience) with active hands-on coding experience aspiring to build expertise in Deep Learning

You can apply for this programme online by submitting your duly filled application.

  1. Education Certificate should indicate the marks obtained in Graduation or Post Graduation with at least 50%.
  2. Latest Pay Slip should specify your date of joining, confirming at least 1 year of work experience. If not, you will have to submit your experience letter(s) indicating the same.

Programme Fee

Participants who are not holding an Indian Passport or are not residing in India will be considered as an International Participant.

Yes, the programme fee can be paid in interest-free installments through our finance partners. Please reach out to your RM to know about the details of the schemes available

The programme fee for PG Level Advanced Certification Programme in Deep Learning is ₹3,60,000 + GST.
For EMI details, Visit Fee Page

No, the application fee is non-refundable.

No, the total programme fee is non-refundable.

Building deep-tech expertise is an absolute necessity for professionals in this fast-changing world continuously disrupted by technology.

Professionals have multiple options for funding.

  1. Sponsored by employer
  2. Self-funded

Based on our experience in enabling thousands of professionals who have participated in our executive programs, self-funding seemed to be the most preferred option, with over 80% choosing this.

Three key reasons why professionals opt for it:

  1. Freedom: To opt for a programme of their choice, at a time of their choice, with an institution of their choice and not be restrained by any organisation’s policy.
  2. Flexibility: To pursue more significant career opportunities even beyond their current employer or to nurture their entrepreneurial ambitions.
  3. Funding: Access to flexible EMI schemes (Paying as low as Rs. 4200 per lac per month).

We have also observed that the commitment levels were higher among self-funding participants.


Module 1

10 Weeks

Mathematical Preliminaries and Data Visualization

  1. Linear Algebra
    Eigenvalues and eigenvectors, singular value decomposition, linear system of equations, rank and null space, pseudo inverse, matrix factorization, matrix calculus.
  2. Probability and Statistics
    Random variables, Probability distributions, Marginal probability, Conditional probability, the chain rule of conditional probabilities, Independence and conditional independence, Expectation, variance and covariance, Common probability distributions, Multivariate distributions, Bayes rule
  3. Numerical Optimization for ML
    Introduction to Numerical Optimization, Modeling --- variables, criteria, constraints, Gradient descent, stochastic gradient descent, Step-size selection, Linear Least squares, Second order methods(Newton, Quasi-newton), Unconstrained and Constrained Optimization, Case study: Support Vector machines.
  4. Data Visualization
    The value of visualization; Visualization design: data types, visualization pipeline, marks and channels, color; Visualization techniques: charts, graph layout, treemaps; Visualization systems

Module 2

2 Weeks

Introductions to Machine Learning

  1. Supervised Learning
    Introduction to SL, How to formulate a SL problem, linear classification, linear regression, loss function, regularization, gradient algorithms, features, non-linear classification, tutorial on using ML packages
  2. Unsupervised Learning
    Introduction to UL, Parameter Estimation, EM Algorithm, learning, mixtures

Module 3

5 Weeks

Deep Learning Architectures

  1. Logistic regression
  2. Neural Networks - CNN, RNN, LSTM
  3. Backpropagation, Deep networks
  4. Regularization, Dropout, Batch Normalization

Module 4

5 Weeks

Deep Learning for Natural Language Processing

  1. Introduction
  2. Distributed word representations
  3. Language Modeling, Convolutional neural networks for Text
  4. GRUs, LSTMs for Language Modeling, Attention and Applications, GPT, BERTs and Variants
  5. Recurrent Neural Networks (Unidirectional and Bidirectional), Machine Translation, POS, Sentence Classification, Text Generation

Module 5

4 Weeks

Deep Learning for Speech and Audio Processing

  1. Audio representations for deep learning
  2. Speech Recognition
  3. End-to-end deep networks
  4. Detection Models

Module 6

5 Weeks

Deep Learning for Computer Vision

  1. Popular CNN architectures
  2. Transfer learning, autoencoders
  3. Object detection,image segmentation
  4. RNN and LSTM for image captioning/video

Module 7

3 Weeks

Deep Reinforcement Learning

  • Introduction to sequential decision making under uncertainty
  • Implementing RL algorithms with deep neural networks
  • Value functions, Finite and infinite Problems

  • Module 8

    4 Weeks

    Deep Learning for IoT/Edge Devices

    1. Overview of various ML hardware for IoT/Edge devices
    2. Energy Efficiency, IoT/Edge Devices
    3. Optimization techniques * ML Model for Edge Devices

    Module 9

    5 Weeks

    Representation Learning

    1. Deep Generative Models I
      Introduction to Generative models, Latent variable models, Variational inference and Auto Encoders
    2. Deep Generative Models II
      Adversarial Learning, Generative Adversarial networks and variants, Normalizing Flows, Applications for image-to-image translation, image inpainting and attribute manipulation, Dataset Augmentation, Wav2Vec
    3. Semi and Self-supervised Learning I
      Consistency Regularization, Weakly supervised learning methods, Domain Adaptation, transfer learning and Domain generalisation, Applications on learning with scarce data
    4. Semi and Self-supervised Learning II
      Self-supervised and Contrastive Representation Learning, Contrastive losses, Hard negative mining, Applications in learning with unlabelled data

    Capstone Projects

    1. COVID-19 detection using acoustic sounds
    2. Use of NLP models for solving simple Math Word Problems
    3. Study of Practical BERT models for Sequence Labeling
    4. Prompt Learning for Vision Language Models
    5. Training a speech recogniser using data from speech synthesis
    6. Training a Voice conversion model
    7. Reinforcement Learning based Task Offloading in Mobile Edge Computing
    8. Anomaly Detection in Industrial IoT
    9. Federated Multi-Armed Bandits based Recommender System
    10. Interpretable GAN Controls
    11. Crafting Adversarial Samples for Deep Models
    12. Monocular Depth Estimation
    13. Text only training for Image Captioning
    14. Zero-shot segmentation using Stable Diffusion
    15. Open set zero shot classification via stable diffusion
    16. Knowledge distillation for Speech Recognition
    17. Time series forecasting via LLMs
    18. Lesion Boundary Segmentation
    19. Neural Models for Legal Judgement Prediction
    20. Optic-Disk-Cup Segmentation and Classification

    Yes, this is a hands-on programme with sessions accompanied by practical industry-oriented exercises and online lab assignments. This helps you to assimilate theory into practice and master the concepts with one-to-one mentoring.

    • LIVE interactive online classes by top faculty help in getting a deep understanding of the subject.
    • Weekly assignments and mini-projects evaluated by mentors help you get an extensive insight into the concepts.
    • LIVE classes enable high-quality peer-learning, personal mentoring, group labs, and hackathons, all on the digital platform.

    Yes, if you would like to solve a problem which you / your organization is facing, you will have to bring the data along. However, if you do not have the necessary data, you can choose from the available curated capstone projects.

    Schedule and Details

    The online sessions will be held every Saturday between 10:00 AM - 1:30 PM & 2:30 PM - 5:30 PM.

    The programme will be delivered through faculty-led interactive live sessions on TalentSprint’s

    A part of the programme will be delivered at the IISc campus during the campus visit.

    All reading material (pre/post session) will be shared regularly through the Online Learning Management System.

    Batch 7 starts in September 2024.

    The total duration of the programme is 10 months.

    A certificate of successful completion is provided upon completion of all requirements of the programme. All examinations and evaluations related to the certification are carried out by Center for Continuing Education at IISc.

    All sessions are conducted by IISc Bangalore faculty. Industry experts may be invited to share their valuable experience.

    Campus Visit

    IISc Campus Visit of 2 Days has been planned towards the end of the programme. A decision on the same will be taken after the programme starts. In case the campus visit is not organized, the on-campus sessions will also be held via live interactive classes.

    The campus visit is a crucial part of this programme. It facilitates an in-class experience of face-to-face sessions. Participants not only get an opportunity to meet the faculty members and network amongst themselves; they also get a chance to immerse in the rich academic environment of the Institute. They can avail the library facilities during their stay and also use the study areas and social spaces open to the entire community on the campus.

    Yes, the campus visit, if scheduled, will be mandatory for the successful completion of the programme. However, if the campus visit is not held the on-campus sessions will be delivered via live interactive classes.

    The campus visit fee will be based on actuals.

    About the Platform

    Do not worry. In case you miss a session, you will be given access to view the recorded version of the session within a specified timeframe.

    75% attendance for the sessions is the minimum attendance criteria for the programme. To complete the programme, you need to complete and submit all program submissions that may be due at your end.

    • An internet-connected device, Computer/Laptop/Tablet/Smartphone, is enough to access the platform.
    • Web-camera and Mic with necessary power backup will be needed for proctored online assessments.

    All participants are required to execute, before the start of the program, an agreement that consists of the standard Programme Terms and Conditions. It has the following components:

    • Part A: Etiquette and Platform Rights
      • Classroom Etiquette & General Policy Guidelines
      • Tools and Platforms in Use
      • Tools and Platforms: Terms of Use
    • Part B: Honor Code
    • Part C: Certification
    • Part D: Programme Fee, Refund and Termination Policy


    IISc (Indian Institute of Science) is India's foremost academic institution for the pursuit of excellence in higher education & research. It offers world-class education in science, engineering, design, and management. IISc became a deemed university in 1958. It is one of the first six institutes to be awarded the Institute of Eminence status. The alumni of this Institute hold significant academic and industry positions around the globe. For more information visit