The Advanced Certificate in Applied Artificial Intelligence & Deep Learning is designed to provide participants with a comprehensive understanding of data analytics and advanced deep learning techniques. Learners will gain expertise in Python for data manipulation and analysis, apply essential statistical methods, and master machine learning algorithms. The programme emphasizes practical experience with tools such as PyTorch and TensorFlow, and incorporates MLOps, and cybersecurity concepts. By engaging in hands-on learning and case studies, participants will be prepared to address real-world data challenges effectively.
Certification from the prestigious IITM Pravartak Technology Innovation Hub of IIT Madras
Comprehensive curriculum covering latest tools, techniques, and algorithms
Hands-on experience with deep learning frameworks
Online live classes will be conducted by faculty from IIT Madras, other IITs, IIMs, and industry experts
A balanced pedagogy of theory, practice, webinars, and projects
Optional one-day Campus Immersion at IIT Madras Research Park
Learning Outcomes:
Master Python programming; Manipulate data using core libraries; Build foundational analysis and visualization skills
Learning Outcomes:
Apply statistical models to data; Infer relationships and trends; Make quantitative decisions using probability
Learning Outcomes:
Clean and prepare raw datasets; Develop effective data visualizations; Communicate insights clearly
Learning Outcomes:
Design and train ML models; Apply predictive analytics; Assess model performance accurately
Learning Outcomes:
Build and train deep neural networks; Tune parameters for optimal performance; Apply DL frameworks
Learning Outcomes:
Apply DL to CV and NLP tasks; Deploy AI models across domains; Compare architectures and performance
Learning Outcomes:
Develop automated ML pipelines; Manage model life cycles; Understand LLMOps for generative models
Learning Outcomes:
Analyze industry AI trends; Evaluate generative and causal AI applications; Design domain-specific AI solutions
Learning Outcomes:
Explain the foundations of Agentic AI, Historical Development of Agentic AI; Identify use cases of adaptive agents
Learning Outcomes:
Build mental models of agent architecture; Implement simple multi-agent flows; Analyze emerging agent technologies
Learning Outcomes:
Establish governance frameworks for AI agents; Measure and optimize performance; Apply ethical principles in agent design
Note: For more details download brochure.
Educational Background:
Graduation or Post Graduation in Engineering, Mathematical and Computational Sciences Min 50% is required in the graduation.
Two alternate weekends per month:
Morning: 10:00 AM - 12:30 PM
Afternoon: 02:00 PM - 04:00 PM
Prof. Babji Srinivasan received his B.Tech degree in instrumentation and control engineering from Madras Institute of Technology, Chennai, India. In 2008, he received the Master's degree in chemical engineering from the Indian Institute of Technology Madras, Chennai, India. He then started his doctoral work at the department of chemical engineering at Texas Tech University Lubbock, TX, USA and received his doctorate in 2011. In 2012, he joined the Indian Institute of Technology Gandhinagar, India as an Assistant Professor at the departments of chemical and electrical engineering. In 2020, Prof. Babji joined the Indian Institute of Technology Madras as an Associate Professor in the department of applied mechanics, IITM.His research interests include cognitive systems engineering, behavioural informatics and human cyber-physical systems.
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Mr. Suresh’s expertise is in the area of data sciences. In a career spanning over twenty-five years, he has helped organizations develop profitable brands and businesses using research and analytics. He has worked in the areas of advertising, market research and analytics with JWT, TNS India, and IBM Daksh. He has been teaching market research as a visiting faculty at various IIM’s, and is also involved in training analytics professionals. Suresh graduated from IIT New Delhi with a degree in Chemical Engineering in 1984 and completed his post- graduate diploma in management from IIM Bangalore in 1988.
Dr. Ranganathan Srinivasan holds a Ph.D. in chemical engineering from Clarkson University, USA, and is currently an adjunct professor at IIT Madras. He has more than 25 years of experience at Honeywell in various roles and is a Lead Consultant at present. His work experience includes the application of machine learning towards productization in the following domains: industrial, building, and supply chain. He is an avid researcher and has been granted 15 US patents, 10 trade secrets, and has an academic citation of over 825. He has been credited with enabling the business of over USD 50 million. His interest in teaching and research was kindled during his MTech at IIT Bombay in 1998 and he has since worked closely with academia.
Prof. Pankaj Dutta is Professor in Decision Sciences and Operations Research at School of Management, Indian Institute of Technology (IIT) Bombay, Mumbai, India. He holds a PhD degree from IIT Kharagpur, India, and a Postdoctoral Fellow from EPFL, Swiss Federal Institute of Technology, Switzerland. He has worked as an INSA Fellow at Karlsruhe Institute of Technology, Germany, and DAAD Visiting Professor at Humboldt University of Berlin, Germany. He served as an associate editor of OPSEARCH and Guest Editor, editorial board member and reviewer of several international journals. He is the Central Council Member of the Operational Research Society of India (ORSI). He has received several merit/best paper awards and research grants and is also a member of ORSI, ISDSI and PMI, India. His current areas of research interest include applied operations research, business analytics, supply chain management, e-commerce, and reverse logistics. He has several publications in international journals like European Journal of Operational Research, Annals of Operations Research, Transportation Research Part E: Logistics and Review, International Journal of Production Research, Journal of Cleaner Production, IEEE Transactions on Engineering Management, International Journal of Information Management, International Transactions in Operational Research, Information Systems Frontiers, Computers & Industrial Engineering and International Journal of Systems Science among others.
Dr. Jayadev did his B. Tech in Electrical and Electronics Engineering from Gayatri Vidya Parishad College of Engineering, Vizag. He did his Masters and PhD from IIT Madras and specializes in the areas of data science, optimization and control engineering. During his Phd, Jayadev was affiliated with the Robert Bosch Center for Data Science & AI, and Systems. He is now working as a Principal Data Scientist in Gyan Data Pvt. Ltd. Jayadev’s research interests include modelling, analysis, optimization, and control of systems, applying tools of machine learning, reinforcement learning and deep learning. His works have been published in multiple international conferences and peer reviewed journals. Jayadev played an active role in content development and tutoring for multiple courses offered by his PhD Guide (Dr. Ramkrishna Pasumarthy) through the NPTEL platform of Govt. of India. He is also a guest faculty for the postgraduate program in Industrial AI at IIT Madras.
Dr. Neelesh Shankar Upadhye is an Assistant Professor at the Department of Mathematics, Indian Institute of Technology, Madras. He obtained his Masters and Doctoral degree in Mathematics from the Indian Institute of Technology Bombay. The major areas of his research interest span across Probabilistic Approximations, Compound Poisson and Compound negative binomial Approximations, Distribution Theory, Negative binomial perturbations and Poisson perturbations, Market Microstructure, Fractals and Data Science. Prior to being a faculty member of IIT Madras, he was a Quantitative Researcher at Dolat Investments Ltd., Mumbai. Dr. Neelesh has co-authored nearly 20 publications based on Probability and Statistics.
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