Overview of AIML Department

VISION

• To develop professionals equipped to build sustainable and intelligent solutions that effectively interact with natural intelligence towards creating a digitally empowered environment for further generations, safeguarding social ethics.

  MISSION

   

• To enable students with the spirit and power of interdisciplinary acumen by integrating a world of knowledge into a world of intelligent systems and subsystems.
• Boost academic outcomes through place-based education and collaborations with established research labs and industries.
• Encourage entrepreneurship efforts among students and develop them into great leaders.

Programming Laboratory

A Programming Laboratory is a dedicated space equipped with computers and software for students to practice coding. It provides hands-on experience, fostering understanding of programming concepts and languages through practical application. Labs often offer support from instructors or assistants to aid learners in their coding endeavors.

Software Used:

C & C++ Language (Windows)

Data Structure Laboratory

A Data Structure Laboratory is a specialized facility where students experiment with organizing and manipulating data efficiently. It offers hands-on practice in implementing and analyzing various data structures like arrays, linked lists, trees, and graphs. This experiential learning enhances comprehension and problem-solving skills in managing complex data arrangements.

Software Used:

C++ Language (Windows / Ubuntu)

Database Management System Laboratory

A Database Management System Laboratory is a dedicated space for students to learn and practice managing databases. It provides hands-on experience in designing, querying, and maintaining databases using software like SQL. This practical setting allows learners to understand database concepts and gain proficiency in handling real-world data storage and retrieval tasks.

Software Used:

MySQL, Oracle (Windows)

Operating System Laboratory

An Operating System Laboratory is a specialized environment where students explore the principles and functionality of operating systems. Through hands-on exercises, they gain practical experience in tasks like process management, memory allocation, file systems, and networking. This immersive learning enhances comprehension of how operating systems facilitate computer functionality and resource management.

Software Used:

C Language, Windows and Ubuntu Terminal.

Data Science Laboratory

A Data Science Laboratory is a dedicated space where students gain hands-on experience in applying statistical, mathematical, and computational techniques to analyze and interpret complex data sets. Through practical exercises, learners develop skills in data visualization, machine learning, and data mining, preparing them for real-world data-driven problem-solving tasks.

Software Used:

Python and R Programming Language

Design and Analysis of Algorithms Laboratory

The Design and Analysis of Algorithms focuses on developing efficient solutions to computational problems. Students study algorithmic design paradigms, analyze their correctness and complexity, and evaluate their performance. Through theoretical study and practical implementation, learners gain expertise in solving diverse computational challenges, preparing them for advanced problem-solving in computer science.

Software Used:

C Language Windows Terminal

Data Analytics with Excel Laboratory

In a Data Analytics with Excel Laboratory, students engage in practical exercises to analyze data using Excel’s functions and features. Through hands-on exploration, they learn to manipulate, visualize, and interpret datasets, acquiring skills in data cleansing, pivot tables, statistical analysis, and visualization techniques, preparing them for data-driven decision-making roles.

Software Used:

Microsoft Excel

Machine Learning Laboratory

In a Machine Learning Laboratory, students delve into hands-on experimentation with algorithms and models to analyze and predict patterns in data. Through practical exercises, they gain proficiency in tasks like data preprocessing, model training, evaluation, and optimization, preparing them for real-world applications in various fields reliant on machine learning techniques.

Software Used:

Python Language

Python Programming Laboratory

The Python Programming Laboratory provides hands-on experience in Python language essentials, data structures, algorithms, and problem-solving techniques. Through practical exercises and projects, students develop proficiency in coding, debugging, and software development, fostering a strong foundation for programming in various domains.

Software Used:

PyCharm and Python Language

Mango DB Laboratory

The Mango DB Laboratory focuses on practical applications of MongoDB, a popular NoSQL database system. Students learn database design, querying, indexing, and scaling techniques through hands-on exercises and real-world projects. This laboratory equips learners with valuable skills for managing large volumes of data in modern software development environments.

Software Used:

Mango DB.

Object Oriented with Java Laboratory

The Object Oriented with Java Laboratory immerses students in Java programming, emphasizing object-oriented principles such as inheritance, encapsulation, and polymorphism. Through hands-on exercises and projects, learners develop proficiency in designing and implementing Java applications, gaining essential skills for building robust and scalable software solutions.

Software Used:

C++ Language

Digital Image Processing Laboratory

The Digital Image Processing Laboratory delves into the theory and application of techniques for manipulating and analyzing digital images. Students explore methods such as filtering, segmentation, and feature extraction through hands-on projects. This laboratory equips learners with skills vital for image enhancement, recognition, and computer vision applications.

Software Used:

Photoshop and Lightroom

Mobile Application Development Laboratory

The Mobile Application Development Laboratory focuses on creating applications for mobile platforms like iOS and Android. Students learn about UI/UX design, programming languages (e.g., Swift, Java, Kotlin), and integrating features such as GPS, sensors, and databases. Through hands-on projects, learners gain proficiency in building functional and user-friendly mobile apps.

Software Used:

Xcode.

• Advanced Machine Learning Techniques: Covering advanced algorithms and methodologies beyond the basics, such as deep learning, reinforcement learning, ensemble methods, and probabilistic graphical models.
• Natural Language Processing (NLP): Exploring techniques for understanding and processing human language, including sentiment analysis, text generation, named entity recognition, and machine translation.
• Computer Vision: Delving into the theory and applications of computer vision, including image classification, object detection, semantic segmentation, and image generation.
• Data Engineering and Management: Providing skills in data preprocessing, feature engineering, data integration, and data governance to effectively manage and prepare data for AI and ML models.
• Model Deployment and Optimization: Teaching methodologies for deploying machine learning models in production environments, including optimization techniques for performance and scalability.
• Ethical and Responsible AI: Addressing ethical considerations, bias mitigation strategies, and regulatory compliance in AI and ML development and deployment.
• Industry Applications and Case Studies: Offering insights into real-world applications of AI and ML across various industries, along with case studies and practical examples to illustrate concepts.
• Hands-On Projects and Workshops: Providing opportunities for participants to apply their learning through hands-on projects, workshops, and hackathons, fostering practical skills and experience.

PROGRAM OUTCOMES (POs)

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 modeling 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.

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)  &  PROGRAM SPECIFIC OUTCOMES (PSOs)

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COs

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