- Image enhancement
- Structure of human eye and vision
- Main goals and challenges
- Introduction to computer vision
- Image processing goals and tasks
- Edge detection
- Color models
Machine Learning for Computer Vision
Learn machine learning from experts from IIT and set your career or startup on the sail by exploring Machine Learning for Computer Vision by NIT Patna.
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Machine Learning in Computer Vision Certification Programme instils a feeling of curiosity and hunger for knowledge related to the fascinating avenue of machine learning in computer-enabled vision. The current domain holds great scope for individuals possessing a budding interest in it and proves to be a coupled breakthrough in providing optimum academic learning. The course will fall as the ideal training course for startup founders, engineers and computer scientists.
The curriculum targets various application domains for the resolution of industrially significant real-life problems while concentrating on its algorithm from the human biological vision. The course deals with a plethora of problems and offers practicable solutions to the same by emphasising on computer vision.
The faculty from IIT and various other eminent institutes discuss at length about various methods of processing, acquiring, analysing, and understanding extraction of data from the real world and digital images to produce information. Learners will find themselves acquiring appreciable knowledge of diverse sub-domains as well which are related to object recognition, motion estimation, and video tracking which candidates will be able to implement in navigation, medicine, and object modelling, amongst other things.
- Online lab and training sessions
- Online/Live lectures sessions by subject experts
- Follow up sessions and discussion forums on research problems and internships
- Comprehensive tutorials and practice notes
- Certification from NIT Patna
- The programme provided by ICT Academy
- Live lectures
- Practice notes
- Discussion forums
Course and certificate fees
The fee details for the course are as follows:
- Machine Learning in Computer Vision Certification Programme comes at a fee of Rs. 500 for academicians or students belonging to general/OBC category and Rs. 250 for academicians or students belonging to SC/ST category.
- Industry professionals and other applicants will have to pay Rs. 1000 if they are from general/OBC category and Rs. 500 if they are from SC/ST category.
Fee Details for Machine Learning in Computer Vision Certification Programme
Fee for academicians/students
Fee for industry people/others
Rs. 500 (for GEN/OBC)
Rs. 250 (for SC/ST)
Rs. 1000 (for GEN/OBC)
Rs. 500 (for SC/ST)
certificate providing authority
Who it is for
Machine Learning in Computer Vision Certification Programme can be pursued by the following groups of people who possess an inclination towards machine learning-
- Startup founders or entrepreneurs striving for a breakthrough in this field.
- Computer scientists conducting research in the domain.
- Engineers who have had a previous brush with machine learning.
Certification Qualifying Details
Candidates will get their certificates of completion after successfully completing the entire programme and submitting all the key things required.
What you will learn
Candidates pursuing Machine Learning in Computer Vision Certification Programme will obtain valuable insights and practical exposure to various facets of machine learning in improving vision, which would go a long way in their academic and professional life. They would learn about the following post-completion-
- The relation between image processing and computer vision
- An introduction to the concepts of machine learning and artificial intelligence
- An introduction to deep learning techniques
- Understanding of convolutional neural network and architectures for computer vision
- Methods of motion detection and depth estimation
- The procedure of detecting objects using CNN
- Different applications of CNN
The admissions for Machine Learning in Computer Vision Certification Programme have been closed. If they resume, candidates can follow the steps mentioned below-
Step 1: Go to the course link- http://www.nitp.ac.in/ict/machinelearning.php and select “Apply Online.”
Step 2: You will have to fill the application form with the requested details.
Step 3: Further process will be notified after you fill the form.
Introduction to Image Processing and Computer Vision (CV)
Introduction to artificial intelligence (AI) and machine learning (ML)
- Image enhancement
- Feature extraction using local patterns and their applications to image processing and cv: image classification
- Supervised and unsupervised learning
- Introduction to artificial intelligence and machine learning
Introduction to deep learning (DL)
- Applications of NN in image processing and cv
- Back propagation
- Types of NNs and limitations
- Basic differences of conventional ml and dl approaches
- Stochastics gradient method and variants, regularization, and optimization
- Feed forward neural networks (NN)
Convolutional neural network architectures (CNN) for CV
- Basic architecture of a convolution neural network CNN as feature extractors
- Introduction to GAN
- Image classification using CNN
- The convolution operation
- Image enhancement and segmentation
Motion detection and depth estimation (de)
- Dl based depth estimation
- Optical flow
- Stereo vision
- Flow net and their versions
Object detection using CNN
- R-CNN, faster r-CNN
- Recent models for object detection
Applications of CNN
- Siamese network and triplet loss
- Face detection and recognition using CNN
- Recent advances
How it helps
Science is a field whose scope never fades, and machine learning is one such branch which has limitless avenues. Machine learning in Computer Vision receives patronage from NIT Patna and is an exquisite professional course aimed at moulding the curiosity of working professionals into a robust and illustrious career in this domain. By the end of this course, learners will get to work on application domains used to decipher some critical real-life problems related to biological vision.
Apart from this, learners will get fruitful insights on various practicalities which would give them information about working in different organisations related to medicines, object modelling, etc. They will receive training with a professional orientation through the live lectures conducted by subject experts possessing seamless knowledge of the field. The tutorials would strengthen their knowledge as they could go through it in case of doubts. Practice notes will further help them in their journey towards becoming an able machine learning professional.
The programme offers online lab and training sessions for exposing course takers to practical nuances. Regular follow up sessions monitor their progress and learning while active discussion forums encourage engagement as they get to learn from each other. Post completion, learners can also work on research problems and internships.
They are as follows-
- Prof. P.K. Biswas, IIT Kharagpur
- Prof. Aparajita Ojha, IIITDM Jabalpur
- Dr. Santosh Kumar Vipparthi, MNIT, Jaipur
- Dr. Santosh Viparthi, MNIT Jaipur
- Prof. Aparajita Ojha, IIITDM Jabalpur
- Dr. Partha Pratim Roy, IIT Roorkee
No, candidates can only make the payment via net banking or offline banking. The details have been shared on the course page.
Candidates are advised to keep a check on the course page for updates regarding the same.
As a part of the curriculum, learners will get to attend practice sessions on implementations of CNN using Tensorflow, Python, and Keras.
The programme has been launched as a joint venture of MNIT Jaipur, IIT Guwahati, PDPM IITDM Jabalpur and NIT Patna.
The course is supported by the Ministry of Electronics and Information Technology, Government of India.
No, the course is entirely online and all the practical sessions will also be online.
No, they can only access the online version of the practice notes which they will receive once they get enrolled.
Learners can make use of an online discussion forum to ask doubts from the faculty which will be responded to there.