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Programmes

Bachelor of Computer Applications (Honours) - Artificial Intelligence and Machine Learning

Overview

The AI/ML (Artificial Intelligence / Machine Learning) Honors program is an undergraduate degree program that focuses on computer applications and advanced techniques in artificial intelligence and machine learning. This specialized program is designed to provide students with a deep understanding of AI/ML concepts, algorithms, and applications, preparing them for careers in cutting-edge fields such as data science, artificial intelligence, and robotics.

This program equips students with the theoretical knowledge, practical skills, and hands-on experience needed to excel in the rapidly growing field of artificial intelligence and machine learning. Graduates are prepared to pursue careers as AI/ML engineers, data scientists, research scientists, and AI consultants in a wide range of industries.

Duration

Four Years / Eight Semesters

Commence of the course

Course Duration

Four Years

Mode of Study

Eligibility

Minimum 50% marks at 10+2 level from recognized Board / Council (Including English & Mathematics / Computer Science / Informatics Practice / Computer Applications / Multimedia & Web).

A relaxation of 5% marks or its equivalent grade may be allowed for those belonging to SC / ST.

Key Areas

Core Computer Science Knowledge:

Develop a strong understanding of fundamental computer science concepts, algorithms, and data structures.

Programming Proficiency:

Acquire proficiency in programming languages commonly used in Artificial Intelligence and Machine Learning. Develop the ability to design, implement, and debug software solutions for Real time-related challenges.

AI & ML Fundamentals:

Gain a comprehensive understanding of key AI & ML concepts, including data preprocessing, supervised and unsupervised algorithms, exploratory data analysis, and statistical modeling.

Mathematical Foundation:

Build a solid foundation in mathematical concepts essential for classification, regression, and clustering such as linear algebra, calculus, statistics, and probability.

Database Management:

Learn to design, implement, and manage databases for efficient data storage and retrieval.

Machine Learning and Predictive Modeling:

Explore machine learning algorithms and techniques for predictive modeling, classification, and clustering.

Data Visualization:

Develop skills in data visualization to effectively communicate insights and findings from data analysis.

Big Data Technologies:

Understand and work with big data technologies and frameworks such as Hadoop and Spark for processing and analyzing large datasets.

Business Intelligence:

Learn how to extract actionable insights from data to support decision-making in a business context.

Ethical and Legal Considerations:

Understand the ethical and legal implications of working with data, including issues related to privacy and data security.

Project Management and Collaboration:

Develop project management skills to plan, execute, and deliver data science projects on time and within scope. Foster collaboration and effective communication within interdisciplinary teams.

Internship and Practical Experience:

Provide opportunities for internships or real-world projects to allow students to apply their knowledge and skills in practical settings.

Project Work:

A capstone project where students apply their skills to solve real-world problems or a significant internship experience.

Programme Objectives

  • To equip students with a well-rounded skill set, preparing them for a successful career in Artificial Intelligence/Machine Learning fields.
  • To provide students with a comprehensive understanding of the theoretical foundations, practical applications, and ethical considerations of AI and ML technologies.

Programme Outcomes

Demonstrate a comprehensive understanding of the theoretical principles, algorithms, and methodologies underlying artificial intelligence and machine learning.

Exhibit proficiency in programming languages commonly used in AI and ML development, such as Python, R, and associated libraries and frameworks.

Analyze complex problems, identify suitable AI/ML techniques, and develop innovative solutions to address real-world challenges across various domains.

Possess the skills to collect, clean, preprocess, and transform data, preparing it for use in machine learning models effectively.

Designing, implementing, and optimizing machine learning models for tasks such as classification, regression, clustering, and reinforcement learning, and evaluating model performance using appropriate metrics.

Develop AI-powered applications and systems, integrating machine learning algorithms into software solutions to automate tasks, enhance decision-making, or provide intelligent functionalities.

Understand the ethical considerations and societal impacts associated with AI and ML technologies and apply ethical principles in the design and deployment of AI systems.

Communicate complex AI/ML concepts and findings effectively to both technical and non-technical audiences, through written reports, oral presentations, and visualizations.

Possess strong teamwork and collaboration skills, capable of working effectively in interdisciplinary teams to tackle multidisciplinary projects or research initiatives.

Programme Structure

Semester I
  • Computing and Information Technology
  • Logical Programming using C
  • Environmental Science
  • Statistics
  • Introduction to Logic
  • Introduction to Scientific writing
Semester II
  • Object Oriented Programming using C++
  • Database Management Systems
  • Operating Systems
  • Artificial Intelligence
  • Discrete Mathematics
Semester III
  • Data Warehousing and Data Mining
  • Java Programming
  • Information Retrieval
  • Linear Algebra
  • Data Structures using OOPs
Semester IV
  • Computer Networks
  • Machine Learning
  • Data Visualization with tableau
  • Python programming
  • Software Engineering
Semester V
  • Cloud Computing
  • Computer Vision
  • Cyber Security
  • Design and Analysis of Algorithm
  • Hands on Project - 1
Semester VI
  • Big Data
  • Image Processing
  • Virtual Environments
  • Mobile Application Development
  • Hands on Project - 2
Semester VII
  • Deep Learning
  • Expert Systems
  • Natural Language Processing
  • Generative AI
  • Internet of Things for AI
Semester VIII
  • Capstone Project
  • Final Presentation -Viva Voce

Curriculum Delivery

Interactive Lectures:

  • Conduct engaging and interactive lectures to introduce theoretical concepts related to computer science, programming languages, and game development.
  • Use multimedia, presentations, and real-world examples to make the content more accessible and interesting.

Hands-On Coding Sessions:

  • Conduct practical coding sessions to reinforce programming skills using languages like Python or R. Hands-on experience is crucial for AI&ML students.

Case Studies and Real-World Projects:

  • Engage students with real-world case studies and projects that simulate actual data science challenges. This helps bridge the gap between theory and application.

Interactive Discussions:

  • Facilitate discussions on current trends, challenges, and ethical considerations in data science. Encourage students to express their opinions and perspectives.

Workshops and Tutorials:

  • Organize workshops and tutorials to provide in-depth guidance on specific tools, frameworks, or methodologies commonly used in AI&ML, such as TensorFlow or sci-kit-learn.

Collaborative Learning:

  • Foster a collaborative learning environment where students work together on group projects, share knowledge, and solve problems collectively.

Online Learning Platforms:

  • Integrate online learning platforms and resources to provide supplementary materials, quizzes, and interactive exercises. Platforms like Jupiter Notebooks can be particularly useful for AI&ML courses.

Field Visits and Internships:

  • Arrange field visits to companies or organizations involved in AI&ML, and encourage students to undertake internships to gain practical experience.

Assessment through Projects and Portfolios:

  • Evaluate students based on their performance in hands-on projects, portfolios, and presentations. This allows them to showcase their practical skills.

Research Assignments:

  • Assign research projects to encourage students to explore advanced topics in AI&ML, fostering a deeper understanding of emerging trends and technologies.

Career Counseling:

  • Provide career counseling sessions to guide students in choosing career paths within the AI & ML industry.
  • Assist with resume building, portfolio development, and interview preparation.

By incorporating these methodologies, we can create a dynamic and engaging learning environment for BCA (Hons.) students, preparing them for success in the rapidly evolving field of AI&ML.

Career Opportunities

  • Artificial Intelligence / Machine Learning Developer
  • Lead Artificial Intelligence / Machine Learning Engineer
  • Artificial Intelligence / Machine Learning Architect
  • Data Scientist
  • Research scientist
  • Data Analyst / Scientist
  • Business Analyst
  • NLP Engineer
  • Business Intelligence Developer
  • Big Data Engineer
  • Big Data Architect
  • Quantitative Analyst
  • AI (Artificial Intelligence) Engineer
  • IoT (Internet of Things) Analyst
  • Computer Vision Engineer

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