SMGOIH
EAMCET CODE : SMED

B.Tech AI & ML

What is B.Tech CSE(AI & ML)

This 4-year undergraduate course of Computer Science & Engineering with specialization in AI & Machine Learning is designed to make the professional technically sound in advanced learning systems that are based on algorithm of Artificial Intelligence. This programme develops the skills of creating fastest working apps or solutions integrated with analytical information ensuring 100% accuracy in the output.

Scope of AI&ML Course

Artificial Intelligence market is expected to grow from USD 21.46 Billion in 2018 to USD 190.61 Billion by 2025.Machine Learning (ML), globally recognized as a key driver of digital transformation, will be responsible for cumulative investments of $58 billion by the end of 2021.Neural networks market will be worth over $23 billion in 2024.
It is not needed to be mentioned that AI has now penetrated in all the horizons of service sector. There is rarely any field which is untouched by Artificial Intelligence hence generating lakhs of vacancies for skilled professionals by 2020.

Career Opportunities

Machine Learning Engineer, Big Data & AI Architect, Big Data Scientist, Artificial Intelligence Engineer, Research Engineer - Artificial Intelligence, Data and AI Consultant, Software Engineer, Machine Learning Engineer, Machine Learning Architect.

Leading Recruiters

Google, Amazon, Facebook, IBM, NIIT, Oracle, Genpact, HDFC Bank, FMCG Companies, E-commerce companies like Snapdeal / Flipkart

Programme Outcomes

Engineering Graduates will be able to:
  • 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
  • 2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
  • 3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
  • 4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
  • 5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.
  • 6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  • 7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
  • 8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
  • 9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • 10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  • 11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
  • 12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

    • Programme Specific Outcomes

  • 1. Identify, analyze, design, and development of systems using principles and concepts of Artificial Intelligence and Machine Learning.
  • 2. Apply the concepts, principles and practices of Artificial Intelligence and Machine Learning and critically evaluate the results with proper arguments, selection of tools and techniques when subjected to loosely defined scenarios.
  • 3. Apply concepts of Artificial Intelligence and Machine Learning models on data for enabling better decision-making.
    • Course Outcomes