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Total Intake 60

AI is an assemblage discipline that covers everything related to making machines more intelligent. Machine Learning (ML) is a subset of AI and is commonly used along with AI. ML refers to an AI system that gets smarter over time by the self-learn-based algorithm. This course introduces modern AI and ML with equal prominence on introductory concepts and applies them to real-world problems. The course will explore the foundation of modern AI and pay adequate attention to the current innovation in machine learning techniques such as deep learning. The course provides AIML knowledge through lectures, hands-on sessions, case studies, and real-world projects.

Objectives

The offered course intends to enclose

  • Ability to acclimatize and innovate tools and systems in the domains of Artificial Intelligence and Machine Learning.
  • Ability to explore research areasand pursue higher education in reputed institutions with AI Specialization.
  • Inculcate ethical and social responsibility to provide solution in the field of Computer Science and Engineering with AI/ML Specialization.

Future Prospects in AIML

On successful completion of AIML graduation students can choose specialization to pursue higher studies in domains like Expert Systems, Natural Language Processing, Neural Networks, Robotics, Fuzzy Logic Systems, etc. Many universities at Global, National and Local level offer specialized courses in M.S., M.Tech, M.E., MBA, and PG Diploma for AIML undergrads.

The course is a stepping stone to participate in digital India initiative by exploring and conducting research in AI based solutions in the domain of smart cities and Smart vehicles and many more.

Students with capitalist reflect can productizethe necessitate solutions in interdisciplinary domains.This course prepares students to explore domain such as Robotics, Expert Systems, Smarter and efficient solutions to the society in the domain of Healthcare, Agriculture etc.

Program Outcomes (PO)

At the successful completion of AIML course an engineering graduate will be able to

  • Perform data exploratory analysis to understand data and identify hidden patterns.
  • Demonstrate the use of the various data analytical tools to identify and visualize interesting patterns from data.
  • Demonstrate interdisciplinary skills across fields such as Statistics, Computer Science, Robotics andMachine Learning, and Computational Logic.
  • Converse ethical practices related to data-driven decision-making.

Value Added Courses

To make students industry ready, at SIESGST we offer interdisciplinary value added courses which help students in gaining practical knowledge in data analytics and their field of interest. The student development programs in Machine Learning with Python, Data Science with R, Text Mining, Cyber Security, Blockchain Technology,etc are designed to empower them to employ computational thinking and data science tools to solve practical business problems.

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