Machine Learning


One of the popular applications of Artificial Intelligence (AI) is Machine Learning (ML), which has been used to develop and advance numerous fields including hospitals and medicine, human resources and recruiting, marketing, media and e-commerce, military, news, customer service, sensors, telecommunications maintenance, transportation, and more. The increased data volumes, advanced algorithms, and improvements in computing power and storage, paved the way for the automation and formal reasoning in computers including decision support systems and smart search systems that can be designed to complement and augment human abilities. In this specialization, the enrolled students learn about the cutting edge technology and apply the taught concepts to create interesting machine learning applications. .

Courses offered:
Semester 1: Discrete Mathematics and Graph Theory, Advanced Computer Networks and Communication, Pattern Recognition, Probability Statistics, Research Methodology
Semester 2: Probabilistic Graphical Models, Analysis and Design of Algorithms, Foundations of Machine Learning, Big Data Analytics, Optimization Techniques, Data Science, Neural Networks and Evolutionary Algorithms, Computer Vision
Semester 3: Reinforcement Learning, Internet of Things, Advanced Kernel Methods, Deep Learning, Cloud Computing, Machine Learning Applications, Topological Data Analysis
Semester 4: Thesis/Dissertation

Research Areas

Machine Learning and Deep learning, Applications of Machine Learning/ Deep Learning in Biological and Medical Applications, Image Processing, Computer Vision & Pattern Recognition, Biomedical Signal Processing applications, Multimedia Signal Processing: Tools and Applications, Audio Signal processing

Funded Research Projects:
1. Artificial Intelligence based Assistive Device to Impart Perceptual Ability to Visually Impaired Person
The visually impaired person faces many challenges in his routine life and technological interventions may help to meet these challenges. An artificial intelligence based fully automatic assistive technology is designed, which can perform scene segmentation and provide auditory inputs about the surrounding in real time. Deep learning based methods are used to train a model that has images of objects, which are of high relevance to the visually impaired. The trained model can be ported to any low cost computing device for real time identification of the objects. The use of the deep learning model makes this assistive technology highly accurate, robust to ambience lighting conditions, different viewing angles and work in real time. In addition to computer vision based techniques for object recognition a distance measuring sensor is also integrated to make the device more comprehensive. The auditory information that is conveyed to the user after the scene segmentation and identification is optimized to get the more information in less interval of time and faster processing of the video frames.
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2. Intelligent Stethoscope: A low cost device based on body auscultation for early medical diagnosis of heart, lung and prenatal health
The developed device integrates a low-cost DSP processor to compute a recording of a small PCG signal from the heart using machine learning models to diagnose and classify the subject’s heart signal as normal or abnormal with type of cardiac disease. The device is based on machine learning training models for automatic classification and is a user friendly interface which generates a comprehensive report stating the patient’s heart condition with a single click. The prototype is practically usable and affordable, making it well suited for medical screening at primary health care centres and remote areas in India for automatic monitoring of cardiac health and early identification of cardiac disorders.
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Quick Links to Talks by Eminent International Scientists:
1. Deep Machine Learning: Processing Unstructured Data by Prof. Cesare Alippi
2. E-Health Tools on Emotional Detection by Prof. Carlos M. Travieso-Gonzalez
3. Towards Artificial Super Intelligence by Prof. Radim Burget
4. Comparison of Machine Learning to Identify Sepsis Patients in the Emergency Department by Prof. Hao Ying
5. Complex Networks by Prof. Ljiljana Trajkovic

Conference Travel Support for Students:
The enrolled students are entitled to publish in peer-reviewed conferences and journals, and the incurred expenses are borne by the institute subject to the Institute/ University rules. The students are also encouraged to enhance their knowledge by attending various workshops, seminars, short term courses, and internships from reputed institutes and organizations.
Scholarships and Awards:
All GATE qualified students get monthly stipend as per AICTE/ University norms. The students get recognition and awards for impactful publications in SCI indexed journals, outstanding contributions through innovations, and best research ideas in various research contests.