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VisionX

Computer Vision & Deep Learning

Teach machines to see.

Co-developed with iHub Anubhuti–IIITD

Next-Gen Internet & Computing

Duration

~3 months

Format

Fully online

Schedule

1.5-hour live classes on alternate days

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Why this programme

Teach machines to see.

A practical path into image and video intelligence, from classical image processing to modern deep learning, detection, segmentation and deployment. Work through real-world use cases without assuming prior computer-vision experience.

Your starting point

For engineering students with basic Python and some familiarity with arrays and maths. You may know what image recognition is, but need not have trained a vision model or used OpenCV or deep learning before.

These notes describe the programme’s intended starting point, not a formal eligibility table. Working professionals are welcome to enquire; no additional entry criteria are implied here.

What you can take forward

  • Process and analyse images with OpenCV and classical techniques.
  • Train and fine-tune CNN and transfer-learning models in PyTorch.
  • Create object-detection and guided segmentation solutions, and test models across varied examples.
  • Explain vision transformers and vision-language models, then optimise and deploy a model for web or edge use.

Curriculum map

Ten modules. One working outcome.

Core topics and module build tasks are the programme pathway. Optional extensions are marked separately and are not promised core instruction.

Core learningHands-on buildOptional extension
01Foundations of Computer Vision

Core topics

Pixels, image shapes and colours, basic Python/OpenCV and image-file handling.

Build task

Create an image-loading, cropping, resizing and conversion tool.

02Image Processing Techniques

Core topics

Filtering, thresholding, edge detection and simple transformations.

Build task

Build a small before-and-after image-processing tool.

Optional extension · not a core promise

SIFT/ORB.

03Deep Learning Essentials

Core topics

Labels, train/test examples, pretrained inference and intuitive model performance.

Build task

Run a pretrained classifier and inspect its failures.

Optional extension · not a core promise

Optimisation mathematics.

04Convolutional Neural Networks

Core topics

Convolution intuition and classification with one supported pretrained CNN.

Build task

Complete a guided image-classification notebook.

Optional extension · not a core promise

Architecture comparison.

05Transfer Learning and Data Strategies

Core topics

Prepare and label a small dataset, augmentation, train/test split and lightweight transfer learning.

Build task

Adapt a pretrained classifier to a custom set of categories.

06Object Detection

Core topics

One stable pretrained object detector, bounding boxes, confidence and precision/recall.

Build task

Create an image or short-video detection demo and examine failure cases.

07Segmentation and Tracking

Core topics

One guided segmentation or tracking demonstration using a pretrained model.

Build task

Explain and test the tool on varied examples.

Optional extension · not a core promise

U-Net, Mask R-CNN and SAM model families.

08Vision Transformers and Multimodal Models

Core topics

Use one vision-language model for captions or visual Q&A; discuss its limits.

Build task

Create a small multimodal demo.

Optional extension · not a core promise

ViT/CLIP architecture and diffusion.

09Deployment and Edge Vision

Core topics

Build a simple inference UI, measure latency, and discuss model errors and privacy.

Build task

Deliver an accessible vision web app.

Optional extension · not a core promise

ONNX, quantisation and edge devices.

10Capstone Project Capstone begins in module 8

Core topics

Create a classification or detection application for a focused use case.

Capstone deliverables

Include a suitable dataset, reproducible inference, metrics, limitations and demo; complete an individual viva.

Tools named as examples or recommendations in the outline are illustrative; the teaching approach may use one supported tool or stack where indicated.

Portfolio & capstone

Evidence you can show

An industry-style classification or detection capstone for a focused use case, with a suitable dataset, reproducible inference, metrics, limitations, demo and report, presented in an individual viva.

Learning rhythm

One hands-on assignment per module, quizzes and a mid-programme assessment. Guided work includes structured and recorded content, live sessions, exercises, coding practice and case studies.

Illustrative next steps

Where this can lead

Entry-level pathways to explore—not placement promises.

  • Computer vision engineer trainee
  • Deep learning engineer
  • Applied AI developer

Certification

Certification with iHub Anubhuti–IIITD

Co-developed with iHub Anubhuti–IIITD

Delivery & assessment

Duration

~3 months

Format

Fully online

Live classes

1.5 hrs, alternate days

Modules

10, capstone from module 8

Each programme runs about 3 months, fully online, with 1.5-hour live classes on alternate days and about 30–35 hours of live instruction. Structured and recorded content sits alongside hands-on exercises, coding work, case studies, quizzes and guided projects. There is one hands-on assignment per module plus quizzes and a mid-programme assessment. The capstone begins in module 8, includes mentor checkpoints, and concludes with a final demo, assessment and individual viva. Certification considers assignments, the assessment, capstone and a minimum attendance requirement.

One assignment per moduleQuizzes + mid-programme assessmentCapstone + individual vivaMinimum attendance requirement

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