Overview

B. Tech in Computer Science and Engineering (Artificial Intelligence and Machine Learning)

The CB. Tech in Computer Science and Engineering (Artificial Intelligence and Machine Learning) programme offers emerging engineers a magnificent array of courses dedicated to breakthroughs in the field of Artificial Intelligence and Machine Learning (AI&ML) with a foundation in Computer Science and Engineering. The full-time, four-year curriculum exposes students to hands-on technology in order to develop applications and solutions for the world we live in.

Technology is one of those fields that never ceases to evolve. It’s a fast-paced branch with a lot of new ideas and experimentation. The B.Tech. Computer Science and Engineering curriculum is cutting-edge, forward-thinking, and industry-ready. This curriculum includes a variety of courses in computation, such as algorithm design and analysis, computer programming languages, software design, and computer hardware.

This specialization will prepare students to create smart machines using a cutting-edge combination of AI and Data Science technologies such as Artificial Intelligence, Machine Learning, Data Science and Statistics, Big Data Analytics, Computer Vision, Business Intelligence, Deep Learning and Reinforce learning, Robotics, Predictive Analytics using R Swarm and Bio-inspired Intelligence, Robotic Process Automation, Genetic Algorithms, Fuzzy Logic and Systems, Natural Language Processing, and Natural Language Processing.

Essential concepts such as data structures, algorithms, OOPS ideas using Java, databases, software engineering, and design procedures are covered in this specialization. Students will also gain a thorough understanding of machine learning and artificial intelligence through solving real-world issues in a range of application domains, such as robotics, computer vision, natural language processing, and so on. Students will gain knowledge of the machine learning pipeline and data, models, algorithms, and empirical research.

Why AI and ML?

Artificial intelligence (AI) and machine learning (ML) are trendy topics in the computer industry. However, artificial intelligence (AI) has a greater impact on the economic sector than our daily lives. In 2014, around $300 million in venture capital was invested in AI firms, a 300 per cent increase over the previous year, according to Bloomberg.

AI can be found in a variety of places, from gaming consoles to the management of large amounts of data at work. Computer scientists and engineers are working hard to instill intelligent behaviour in machines, allowing them to think and respond in real-time. Google and Facebook, for example, have made significant investments in AI and machine learning and are actively incorporating it into their businesses.

But this is only the beginning; over the next few years, AI may find its way into one product after another.

The programme, B.Tech. CSE (Artificial Intelligence & Machine Learning) aims to:

• Demonstrate technical abilities, proficiency in AI & ML, and team management capability with good communication in a work environment after a few years of graduation.
• Support a country’s economic progress by launching a business with a commitment to lifelong learning.
• Conduct research in advanced AI and machine learning areas while also addressing society’s basic needs.

 

Graduates of the B.Tech. CSE (Artificial Intelligence & Machine Learning) programme will be able to:

• Apply knowledge of mathematics, science, and engineering fundamentals, and solve computer science and engineering specialization problems in Artificial Intelligence & Machine Learning after successfully completing the programme.
• Identify, formulate, study research material, and analyze difficult engineering problems using first principles of mathematics, natural sciences, and engineering sciences to achieve justified findings.
• Design solutions for complex technical challenges and system components or processes that meet the given requirements while considering public health and safety and cultural, socioeconomic, and environmental factors.
• Using research-based knowledge and research methodologies such as experiment design, data analysis and interpretation, and information synthesis to give valid results.
• Create, choose, and apply relevant methodologies, resources, and current engineering and IT technologies to complex engineering processes, including prediction and modelling, while keeping restrictions in mind.
• Assess societal, health, safety, legal, and cultural issues and the duties associated with professional engineering activity, using reasoning informed by contextual knowledge.
• Understand the societal and environmental implications of professional engineering solutions, and demonstrate understanding of and need for sustainable development.
• Apply ethical concepts and adhere to engineering practice’s professional ethics, duties, and conventions.
• Individually and as a member or leader in different teams and transdisciplinary situations, perform well.
• Communicate effectively with the engineering community and society at large on complicated engineering operations, such as reading and creating good reports and design documentation, giving and receiving clear directions.
• Demonstrate knowledge and comprehension of engineering and management principles and how to apply them to one’s own work, as a team member and leader, to manage projects and in interdisciplinary settings.
• Recognize the necessity for autonomous and life-long learning in the broader context of technological change, and have the preparation and ability to do so.