Vision

To bring forth technically versatile, Research oriented, Industry ready engineers in the field of Artificial Intelligence and Machine Learning.

Mission

• Facilitate modern infrastructure and versatile learning resources to produce self-sustainable professionals

•  Facilitate project based learning and skill upgradation through industry  collaborations

•  Inculcate professional ethics, leadership qualities and practice lifelong learning

• PSO1: Graduates will have the ability to adapt, contribute and innovate ideas in the field of Artificial Intelligence and Machine Learning.

• PSO2: To provide a concrete foundation and enrich their abilities to qualify for Employment, Higher studies and Research in various domains of Artificial Intelligence and Machine Learning such as Data Science, Computer Vision, Natural Language Processing with Ethical Values.

• PSO3: Graduates will acquire the practical proficiency with niche technologies and open-source platforms and to become Entrepreneur in the domain Artificial Intelligence and Machine Learning.

• PEO1: Attain proficiency in professional practice

• PEO2: Practice technical skills to identify, analyze and solve complex problems related to Artificial Intelligence and Machine Learning.

• PEO3: Emerge as an Individual or a team member with societal concerns, ethics and motivated for holistic learning.

Module 1

Intelligent Agents: Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of Environments, The Structure of Agents

Problem Solving: Game Paying

Module 2

Uncertain knowledge and Reasoning: Quantifying Uncertainty, Acting under Uncertainty , Basic Probability Notation, Inference Using Full Joint Distributions, Independence, Bayes Rule and Its Use The Wumpus World Revisited.

Module 3

Probabilistic Reasoning: Representing Knowledge in an Uncertain Domain, The Semantics of Bayesian Networks , Efficient Representation of Conditional Distributions Exact Inference in Bayesian Networks, Approximate Inference in Bayesian Networks.

Module 4

Perception: Image Formation, Early Image-Processing Operation, Object Recognition by Appearance, Reconstructing the 3DWorld. Object Recognition from Structural Information, Using Vision.

Module 5

Overview and language modeling: Overview: Origins and challenges of NLP-Language and Grammar-Processing Indian Languages- NLP Applications-Information Retrieval. Language Modeling: Various Grammar- based Language Models-Statistical Language Model.