Vision Mission of the Department
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
Program Specific Outcomes (PSOs):
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.
Program Educational Objectives (PEOs):
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.
Course Objectives
Demonstrate the proficiency with statistical analysis of data to derive insight from results and interpret the data findings visually.
Utilize the skills in data management by obtaining, cleaning and transforming the data.
Make use of machine learning models to solve the business-related challenges.
Experiment with decision trees, neural network layers and data partition.
Demonstrate how social clustering shape individuals and groups in contemporary society.
Course Outcomes
At the end of the course the student will be able to:
Identify and demonstrate data using visualization tools.
Make use of Statistical hypothesis tests to choose the properties of data, curate and manipulate data.
Utilize the skills of machine learning algorithms and techniques and develop models.
Demonstrate the construction of decision tree and data partition using clustering.
Experiment with social network analysis and make use of natural language processing skills to develop data driven applications.