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BCA in Artificial Intelligence Machine Learning Ai Ml at Adarsh Institute of Management and Information Technology
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning (AI & ML) at Adarsh Institute of Management and Information Technology Bengaluru?
This Artificial Intelligence & Machine Learning program at ADARSH INSTITUTE OF MANAGEMENT AND INFORMATION TECHNOLOGY focuses on equipping students with advanced skills in designing, developing, and deploying AI and ML solutions. Catering to the burgeoning Indian tech industry, it integrates foundational computer science with specialized knowledge in intelligent systems, preparing graduates for high-demand roles. The program emphasizes practical, application-oriented learning relevant to India''''s digital transformation.
Who Should Apply?
This program is ideal for fresh graduates from science or commerce backgrounds with a keen interest in logical reasoning and problem-solving, eager to enter the dynamic AI/ML sector. It also benefits working professionals seeking to upskill in cutting-edge technologies and career changers aiming to transition into high-growth areas like data science, machine learning engineering, or AI research within the Indian tech landscape.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as AI Engineers, Machine Learning Developers, Data Scientists, or AI Research Analysts, with entry-level salaries typically ranging from INR 4-7 LPA, growing significantly with experience. The program aligns with industry demands for skilled AI professionals, fostering growth trajectories in top Indian IT companies, startups, and research institutions, potentially leading to professional certifications in cloud AI platforms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand C and Python programming concepts, practicing extensively on online coding platforms. Focus on logic building, data structures, and algorithms to build a strong base for advanced AI/ML concepts.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, Jupyter Notebook
Career Connection
Strong programming skills are non-negotiable for AI/ML roles, serving as the backbone for implementing complex algorithms and solutions in coding rounds during placements.
Build a Strong Mathematical Foundation- (Semester 1-2)
Pay close attention to Discrete Mathematics. Understand linear algebra, calculus, and probability concepts independently through online courses and textbooks. These are crucial for comprehending ML algorithms.
Tools & Resources
Khan Academy, NPTEL courses, MIT OpenCourseware (Mathematics for Computer Science)
Career Connection
A solid grasp of mathematics is essential for understanding the theoretical underpinnings of AI/ML, enabling you to design, debug, and optimize models, crucial for advanced research and development roles.
Engage in Peer Learning & Technical Clubs- (Semester 1-2)
Form study groups to discuss complex topics and solve problems together. Actively participate in the college''''s Computer Science or AI/ML clubs. Attend workshops and seminars to get early exposure to industry trends and network with peers and seniors.
Tools & Resources
College technical clubs, Discord/WhatsApp study groups
Career Connection
Networking and collaborative learning enhance problem-solving skills, provide exposure to diverse perspectives, and help build a professional network beneficial for internships and job referrals in India.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Start working on small AI/ML projects using Python libraries (e.g., scikit-learn, pandas). Participate in beginner-friendly Kaggle competitions to apply theoretical knowledge to real-world datasets and learn from community solutions.
Tools & Resources
Kaggle, GitHub, Google Colab, scikit-learn documentation
Career Connection
Practical projects and competition experience are vital for building a strong portfolio, showcasing your ability to apply ML concepts, and gaining recognition in the Indian data science community, attracting recruiters.
Focus on Specialization-Specific Skills- (Semester 3-5)
Deepen your understanding of specific AI/ML areas like Deep Learning and NLP. Explore frameworks like TensorFlow/PyTorch, and dedicate time to understanding their intricacies. Complete online certifications in these areas.
Tools & Resources
Coursera (Deep Learning Specialization), edX, Udemy, TensorFlow/PyTorch official tutorials
Career Connection
Specialized skills are highly valued in the Indian job market. Certifications and in-depth knowledge of popular frameworks make you a competitive candidate for roles requiring specific AI/ML expertise.
Seek Industry Internships- (Semester 4-5 (during breaks))
Actively search for internships in startups or smaller firms in Bengaluru during semester breaks. Focus on gaining hands-on experience in data analysis, model building, or AI application development. Even unpaid internships offer invaluable learning.
Tools & Resources
Internshala, LinkedIn Jobs, AngelList India
Career Connection
Internships provide crucial industry exposure, help build a professional network, and often lead to pre-placement offers or strong recommendations, significantly boosting your placement prospects in India.
Advanced Stage
Develop a Capstone AI/ML Project- (Semester 6)
Identify a real-world problem or an innovative idea for your final year project. Design, develop, and deploy a robust AI/ML solution, documenting every phase meticulously. Aim for a publishable quality project.
Tools & Resources
Jupyter Notebook, Cloud Platforms (AWS/Azure/GCP), Docker, Git
Career Connection
A strong capstone project is a centerpiece of your resume, demonstrating your full capabilities from problem identification to deployment. It''''s a key talking point in Indian company interviews and proof of your expertise.
Prepare for Placements Strategically- (Semester 5-6)
Start preparing for technical interviews, aptitude tests, and group discussions well in advance. Practice coding challenges, revise core computer science concepts, and prepare behavioral answers for common HR questions, tailored for Indian company recruitment drives.
Tools & Resources
LeetCode, Interviewer.io, Placement cell resources, Mock interview sessions
Career Connection
Thorough and strategic placement preparation is critical for securing desirable job offers in India''''s competitive tech industry, ensuring you can perform confidently in all stages of the recruitment process.
Stay Updated with AI/ML Research & Industry Trends- (Throughout the program, especially Semester 5-6)
Regularly follow leading AI/ML research papers, blogs, and industry news. Attend virtual conferences or webinars relevant to AI/ML in India. Understand the latest advancements and their practical applications to stay competitive.
Tools & Resources
ArXiv, Medium (AI/ML blogs), Analytics India Magazine, LinkedIn Pulse
Career Connection
Staying updated demonstrates proactive learning and passion, which are highly valued by recruiters. It helps you contribute innovative ideas in job roles and keeps your skills relevant in a rapidly evolving field, ensuring long-term career growth in India''''s tech landscape.
Program Structure and Curriculum
Eligibility:
- A candidate who has passed the two years Pre-University Examination or equivalent as recognized by Bengaluru City University with a minimum of 35% of marks.
Duration: 6 semesters / 3 years
Credits: 140 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA1C1L1 | Indian Language - I | Ability Enhancement Compulsory Course (AECC) | 3 | Language Fundamentals, Grammar and Vocabulary, Comprehension Skills, Composition and Expression, Literary Appreciation |
| 20BCA1C2L2 | English - I | Ability Enhancement Compulsory Course (AECC) | 3 | Basic Communication Skills, Grammar and Syntax, Reading and Listening Comprehension, Paragraph Writing, Introduction to Literary Forms |
| 20BCA1C3P | Fundamentals of Computers | Discipline Specific Core (DSC) | 4 | Introduction to Computers, Hardware and Software Concepts, Operating System Basics, Number Systems and Data Representation, Internet and Web Fundamentals |
| 20BCA1C4P | C Programming | Discipline Specific Core (DSC) | 4 | C Language Fundamentals, Control Structures and Loops, Functions and Modularity, Arrays and Strings, Pointers and Structures |
| 20BCA1C5T | Discrete Mathematics | Discipline Specific Core (DSC) | 4 | Set Theory and Relations, Mathematical Logic, Graph Theory, Combinatorics and Probability, Recurrence Relations |
| 20BCA1C6PP | C Programming Lab | Lab | 2 | C Program Implementation, Debugging and Testing, Problem-Solving with C, File Handling in C, Basic Data Structure Programs |
| 20BCA1C7PP | Computer Fundamentals Lab | Lab | 2 | Operating System Installation, MS Office Applications, Internet Browsing and Email, Hardware Familiarization, Troubleshooting Basics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA2C1L1 | Indian Language - II | Ability Enhancement Compulsory Course (AECC) | 3 | Advanced Grammar, Literary Texts Analysis, Cultural Contexts of Language, Public Speaking and Dialogue, Translation Exercises |
| 20BCA2C2L2 | English - II | Ability Enhancement Compulsory Course (AECC) | 3 | Advanced English Grammar, Business Communication, Report and Proposal Writing, Presentation Skills, Group Discussion Techniques |
| 20BCA2C3P | Data Structures | Discipline Specific Core (DSC) | 4 | Introduction to Data Structures, Arrays, Linked Lists, Stacks, Queues, Trees and Binary Trees, Graphs and Graph Algorithms, Sorting and Searching Algorithms |
| 20BCA2C4P | Database Management System | Discipline Specific Core (DSC) | 4 | DBMS Concepts and Architecture, Entity-Relationship Model, Relational Model and Algebra, Structured Query Language (SQL), Normalization and Transaction Management |
| 20BCA2C5T | Object Oriented Programming with C++ | Discipline Specific Core (DSC) | 4 | OOP Concepts and Principles, Classes and Objects, Inheritance and Polymorphism, Constructors and Destructors, Exception Handling and File I/O |
| 20BCA2C6PP | Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation and Traversal, Sorting and Searching Practice |
| 20BCA2C7PP | DBMS Lab | Lab | 2 | SQL Querying and Data Definition, Data Manipulation Language (DML), Stored Procedures and Functions, Trigger and View Implementation, Database Design Exercises |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA3C1E | Environmental Studies | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources and Conservation, Climate Change and Global Issues, Environmental Ethics and Policies |
| 20BCA3C2T | Operating System | Discipline Specific Core (DSC) | 4 | Operating System Functions, Process Management and Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency Control |
| 20BCA3C3T | Python Programming | Discipline Specific Core (DSC) | 4 | Python Language Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, File Handling and Exception Handling |
| 20BCA3C4T | Computer Networks | Discipline Specific Core (DSC) | 4 | Network Models (OSI/TCP-IP), Physical Layer and Data Transmission, Data Link Layer Concepts, Network Layer and IP Addressing, Transport Layer and Application Layer Protocols |
| 20BCA3C5PP | Python Programming Lab | Lab | 2 | Python Scripting and Automation, Data Analysis with Libraries (Numpy, Pandas), Web Scraping with Python, GUI Development with Tkinter, Database Connectivity in Python |
| 20BCA3C6PP | Operating System & Networks Lab | Lab | 2 | Linux Commands and Utilities, Shell Scripting, Network Configuration and Troubleshooting, Socket Programming Basics, Network Security Tools |
| 20BCA3S1AE | Introduction to Artificial Intelligence | Skill Enhancement Course (SEC) | 3 | History and Foundations of AI, Intelligent Agents and Environments, Problem-Solving through Search, Knowledge Representation and Reasoning, Introduction to Machine Learning |
| 20BCA3O1E | Open Elective Course - I (e.g., Office Automation, Web Designing) | Open Elective | 3 | Productivity Software Usage, Data Management in Spreadsheets, Presentation Design Principles, HTML and CSS Fundamentals, Basic Web Page Layout |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA4C1T | Software Engineering | Discipline Specific Core (DSC) | 4 | Software Development Life Cycle, Requirements Analysis and Specification, Software Design Principles, Software Testing and Quality Assurance, Project Management and Maintenance |
| 20BCA4C2T | Java Programming | Discipline Specific Core (DSC) | 4 | Java Language Fundamentals, Object-Oriented Programming in Java, Packages, Interfaces, and Exception Handling, Multithreading and Synchronization, Applets and GUI Programming |
| 20BCA4C3T | Data Warehousing and Data Mining | Discipline Specific Core (DSC) | 4 | Data Warehousing Concepts, OLAP and Multidimensional Data, Data Preprocessing Techniques, Association Rule Mining, Classification and Clustering Algorithms |
| 20BCA4C4PP | Java Programming Lab | Lab | 2 | Java Program Development, GUI Application Building (Swing/AWT), Database Connectivity (JDBC), Web Application Development (Servlets), Exception Handling Practice |
| 20BCA4C5PP | DWH & DM Lab | Lab | 2 | ETL Process Implementation, Data Cleaning and Integration, OLAP Cube Operations, Data Mining Algorithm Application, Data Visualization Tools (e.g., Weka) |
| 20BCA4S1AE | Machine Learning | Skill Enhancement Course (SEC) | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Introduction to Neural Networks, Practical Machine Learning Implementations |
| 20BCA4O1E | Open Elective Course - II (e.g., E-Commerce, Android Programming) | Open Elective | 3 | E-Commerce Models and Strategies, Online Payment Systems, Mobile Application UI/UX, Android Component Development, App Deployment Basics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA5C1T | Web Programming (PHP/ASP.NET) | Discipline Specific Core (DSC) | 4 | Client-Side Scripting (HTML, CSS, JavaScript), Server-Side Scripting (PHP/ASP.NET), Database Integration with Web Applications, Session Management and User Authentication, Web Security Fundamentals |
| 20BCA5D1AE | Deep Learning | Discipline Specific Elective (DSE) - AI & ML | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) |
| 20BCA5D2AE | Natural Language Processing | Discipline Specific Elective (DSE) - AI & ML | 4 | NLP Fundamentals and Challenges, Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging and Parsing, Sentiment Analysis and Text Classification |
| 20BCA5C2PP | Web Programming Lab | Lab | 2 | Dynamic Web Page Development, Form Handling and Validation, Database Connectivity for Web Apps, User Login and Session Management, Web Application Deployment |
| 20BCA5D3PP | Deep Learning & NLP Lab | Lab | 2 | Implementing Neural Networks (TensorFlow/PyTorch), CNN for Image Classification, RNN for Sequence Data, NLP Task Implementation (NLTK, SpaCy), Building Simple Chatbots |
| 20BCA5S1AE | Data Visualization | Skill Enhancement Course (SEC) | 3 | Principles of Data Visualization, Types of Charts and Graphs, Data Storytelling Techniques, Using Tools like Tableau/Power BI, Creating Interactive Dashboards |
| 20BCA5O1E | Open Elective Course - III (e.g., Cloud Computing, IoT) | Open Elective | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization and Cloud Security, IoT Architecture and Protocols, Sensor Networks and Data Collection, Big Data Analytics for IoT |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BCA6C1T | Mobile Application Development | Discipline Specific Core (DSC) | 4 | Mobile OS Architecture (Android/iOS), User Interface (UI) Design for Mobile, Data Storage and Connectivity, API Integration for Mobile Apps, App Deployment and Monetization |
| 20BCA6D1AE | Reinforcement Learning | Discipline Specific Elective (DSE) - AI & ML | 4 | Introduction to Reinforcement Learning, Markov Decision Processes (MDPs), Q-Learning and SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| 20BCA6D2AE | Computer Vision | Discipline Specific Elective (DSE) - AI & ML | 4 | Image Processing Fundamentals, Feature Detection and Description, Object Detection Algorithms, Image Segmentation, Facial Recognition and Pose Estimation |
| 20BCA6C2PP | Mobile Application Development Lab | Lab | 2 | Android Studio and SDK Usage, Activity Lifecycle and Intents, Layout Design and Widgets, Database Operations (SQLite), API Integration and Testing |
| 20BCA6D3PP | RL & Computer Vision Lab | Lab | 2 | Implementing RL Agents, OpenCV for Image Manipulation, Object Recognition with Pre-trained Models, Image Annotation Tools, Real-time Computer Vision Projects |
| 20BCA6D4PP | Project Work | Project | 6 | Project Planning and Management, System Design and Architecture, Implementation and Coding, Testing and Debugging, Documentation and Presentation |
| 20BCA6O1E | Open Elective Course - IV (e.g., Cryptography, Human Computer Interaction) | Open Elective | 3 | Principles of Cryptography, Symmetric and Asymmetric Key Ciphers, Digital Signatures and Certificates, HCI Design Principles, Usability Testing and Evaluation |
