.png&w=1920&q=75)
B-E in Computer Science Engineering Ai Ml at Rajarajeswari College of Engineering
Bengaluru, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Computer Science & Engineering (AI & ML) at Rajarajeswari College of Engineering Bengaluru?
This Computer Science & Engineering (AI & ML) program at RajaRajeswari College of Engineering focuses on equipping students with a robust foundation in artificial intelligence and machine learning principles. The curriculum integrates theoretical knowledge with practical applications, preparing graduates to address complex challenges in various Indian industries, from healthcare to finance, leveraging the rapidly growing demand for AI expertise in the nation.
Who Should Apply?
This program is ideal for aspiring engineers and innovators, particularly fresh graduates seeking entry into the dynamic fields of AI and ML. It also suits working professionals looking to upskill or career changers transitioning into data science and intelligent systems. Candidates should typically possess a strong aptitude in mathematics, logical reasoning, and basic programming, aiming for cutting-edge roles in tech.
Why Choose This Course?
Graduates of this program can expect to pursue promising career paths as AI engineers, Machine Learning specialists, Data Scientists, and Deep Learning researchers within India. Entry-level salaries typically range from INR 4-8 lakhs per annum, with significant growth trajectories in leading IT firms, startups, and R&D divisions. The program also aligns with certifications in popular AI/ML platforms like TensorFlow and AWS ML.
Student Success Practices
Foundation Stage
Master Core Programming & Data Structures- (Semester 1-2)
Dedicate significant time to fundamental programming concepts (C, Java) and data structures. Practice extensively on online coding platforms like HackerRank and LeetCode to build problem-solving muscle.
Tools & Resources
CodeChef, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong fundamentals are the bedrock for any software development or AI/ML role, crucial for cracking technical interviews.
Form Study Groups for Conceptual Clarity- (Semester 1-2)
Collaborate with peers on complex mathematical and physics concepts. Teaching others reinforces your own understanding, and discussing problems often uncovers new perspectives.
Tools & Resources
Whiteboards, online collaboration tools (Google Meet, Zoom), textbooks
Career Connection
Enhances teamwork and communication skills, vital for collaborative project environments in the industry.
Explore Basic Electronics and Circuits- (Semester 1-2)
While AIML is software-centric, understanding the underlying hardware through basic electrical and electronics engineering is valuable. Engage in simple circuit projects and simulations.
Tools & Resources
Tinkercad, LTspice, YouTube tutorials on basic electronics projects
Career Connection
Beneficial for roles involving embedded AI, IoT, or hardware-accelerated machine learning, which are growing areas in India.
Intermediate Stage
Dive Deep into AI/ML Foundations with Projects- (Semester 3-5)
Go beyond theoretical knowledge of AI and Machine Learning. Implement algorithms from scratch using Python, and undertake small projects demonstrating core concepts like regression, classification, and basic neural networks.
Tools & Resources
Python, scikit-learn, Jupyter Notebooks, Kaggle datasets, Coursera/edX courses on ML
Career Connection
Builds a practical portfolio, making you a strong candidate for internships and entry-level AI/ML engineering roles.
Participate in Hackathons and Coding Competitions- (Semester 4-5)
Actively seek out and participate in college-level or national hackathons (e.g., Smart India Hackathon) and coding challenges. This provides hands-on experience under pressure and exposure to real-world problems.
Tools & Resources
DevPost, HackerEarth, GitHub, collaborative IDEs
Career Connection
Develops problem-solving skills, builds a network, and creates visible achievements for your resume, attracting attention from Indian tech recruiters.
Build a Professional Online Presence- (Semester 4-5)
Create a professional LinkedIn profile, connect with industry experts and alumni, and start a GitHub repository to showcase your projects and code. Engage in relevant online discussions.
Tools & Resources
LinkedIn, GitHub, Medium (for technical blogs)
Career Connection
Essential for networking, discovering internship opportunities, and attracting potential employers for placements in India''''s competitive job market.
Advanced Stage
Specialize through Advanced AI/ML Projects & Research- (Semester 6-8)
Focus your major project on a specific area of AI/ML (e.g., Deep Learning for computer vision, Reinforcement Learning for robotics, NLP). Aim for novel approaches or apply techniques to unique Indian datasets.
Tools & Resources
TensorFlow, PyTorch, Keras, OpenCV, specialized research papers (ArXiv)
Career Connection
Develops expert-level skills and creates a strong, specialized portfolio, making you highly valuable for niche roles in AI/ML and research.
Engage in Internships and Industry Training- (Semester 6-7 (during breaks or designated periods))
Secure at least one substantial internship (6-8 weeks) at an AI/ML company or a relevant R&D division in India. Focus on gaining real-world project experience and understanding industry workflows.
Tools & Resources
College placement cell, Internshala, LinkedIn Jobs, direct company applications
Career Connection
Provides crucial industry exposure, builds a professional network, and often leads to pre-placement offers, significantly boosting career launch.
Prepare for Placements with Mock Interviews and Aptitude Training- (Semester 7-8)
Systematically prepare for campus placements by practicing aptitude tests, technical interviews (data structures, algorithms, ML concepts), and HR rounds. Participate in mock interviews with faculty and seniors.
Tools & Resources
India-specific aptitude test books, GeeksforGeeks, InterviewBit, company-specific previous year papers
Career Connection
Increases chances of securing placements in top Indian and multinational companies, ensuring a smooth transition from academics to professional life.
Program Structure and Curriculum
Eligibility:
- PUC/10+2 with Physics and Mathematics as compulsory subjects with Chemistry/Biology/Biotechnology/Electronics/Computer Science as optional subjects with English as one of the languages of study and obtained a minimum of 45% marks in aggregate in the optional subjects (40% for SC/ST/OBC category candidates).
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAT101 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| BPHYC102 | Engineering Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Superconductivity, Nanomaterials |
| BCHYC102 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Chemical Thermodynamics, Water Technology, Polymer Chemistry |
| BECVC103 | Basic Electrical & Electronics Engineering | Core | 4 | DC Circuits, AC Fundamentals, Semiconductor Devices, Operational Amplifiers, Digital Logic Basics |
| BCSC104 | Programming for Problem Solving | Core | 3 | C Programming Fundamentals, Control Structures, Functions and Modularity, Arrays and Strings, Pointers and Structures |
| BCSL106 | Computer Aided Engineering Graphics | Lab | 2 | Orthographic Projections, Isometric Projections, Sectional Views, CAD Software Tools, Assembly Drawings |
| BPHYL107 | Engineering Physics Laboratory | Lab | 1 | Optical Fiber Characteristics, Laser Wavelength Measurement, PN Junction Diode, Transistor Characteristics, Hall Effect |
| BCHYL107 | Engineering Chemistry Laboratory | Lab | 1 | Water Hardness Determination, Potentiometric Titration, Conductometric Titration, Viscosity of Liquids, Synthesis of Polymers |
| BHSL108 | Professional Skill Development | Core | 1 | Communication Skills, Presentation Techniques, Group Discussion Strategies, Resume Building, Interview Preparation |
| BPCLC105 | Professional Communication & Life Skills | Core | 2 | English Grammar and Vocabulary, Listening and Reading Skills, Public Speaking, Soft Skills for Professionals, Interpersonal Communication |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAT201 | Engineering Mathematics – II | Core | 4 | Linear Algebra, Multiple Integrals, Vector Integration, Complex Analysis, Probability and Statistics |
| BECSC202 | Analog & Digital Electronics | Core | 4 | Diode Circuits, Transistor Biasing, Operational Amplifier Applications, Logic Gates and Boolean Algebra, Combinational and Sequential Circuits |
| BCSC203 | Data Structures | Core | 4 | Arrays and Pointers, Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| BCSC204 | Object Oriented Programming with Java | Core | 4 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, File I/O and Collections |
| BMES205 | Elements of Mechanical Engineering | Core | 4 | Thermodynamics Basics, Power Transmission Devices, IC Engines, Refrigeration and Air Conditioning, Introduction to Robotics |
| BCSL206 | Data Structures Laboratory | Lab | 1 | Implementing Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Programs |
| BCSL207 | Object Oriented Programming with Java Laboratory | Lab | 1 | Implementing OOP Concepts, GUI Programming with Swing/AWT, Exception Handling Programs, Multi-threading, Database Connectivity (JDBC) |
| BMEL208 | Elements of Mechanical Engineering Laboratory | Lab | 1 | Study of IC Engines, Refrigeration Cycle Demonstration, Measurement using Vernier/Micrometer, Welding Practice, Lathe Operations |
| BUHVC209 | Universal Human Values | Core | 1 | Understanding Human Values, Harmony in Human Being, Ethics in Life, Professional Ethics, Global Social Issues |
| BSCL210 | Constitution of India | Core | 0 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Legislature, Judiciary in India |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAT301 | Transforms and Linear Algebra | Core | 4 | Fourier Series and Transforms, Z-Transforms, Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors |
| BCPC302 | Database Management Systems | Core | 4 | Relational Model, SQL Queries, ER Modeling, Normalization, Transaction Management |
| BCPC303 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| BCPC304 | Computer Organization and Architecture | Core | 4 | CPU Architecture, Memory Hierarchy, Input/Output Organization, Pipelining, Instruction Set Architectures |
| BAIC305 | Introduction to Artificial Intelligence | Core | 3 | History and Foundations of AI, Intelligent Agents, Search Algorithms (Heuristic), Knowledge Representation, Introduction to Machine Learning |
| BCPL306 | Database Management Systems Laboratory | Lab | 1 | SQL Data Definition Language, SQL Data Manipulation Language, Joins and Subqueries, PL/SQL Programming, Triggers and Stored Procedures |
| BAIL307 | Introduction to Artificial Intelligence Laboratory | Lab | 1 | Python Programming for AI, Implementing Search Algorithms, Knowledge Representation Techniques, Logic Programming (Prolog/Python), Simple Expert Systems |
| BIPH308 | Indian Constitution and Professional Ethics | Core | 0 | Constitutional Framework, Fundamental Rights, Professional Ethics in Engineering, Cyber Laws, Intellectual Property Rights |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAT401 | Probability, Statistics and Queuing Theory | Core | 4 | Probability Distributions, Hypothesis Testing, Correlation and Regression, Stochastic Processes, Queuing Models |
| BAIC402 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Model Evaluation and Validation, Ensemble Methods |
| BCPC403 | Design and Analysis of Algorithms | Core | 4 | Algorithm Design Paradigms, Asymptotic Notations, Graph Algorithms, Dynamic Programming, Complexity Classes (P, NP) |
| BCPC404 | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Routing Protocols, Congestion Control, Network Security Fundamentals |
| BAIC405 | Data Warehousing and Mining | Core | 3 | Data Warehousing Concepts, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Prediction |
| BAIL406 | Machine Learning Laboratory | Lab | 1 | Implementing Regression Models, Implementing Classification Algorithms, Clustering Techniques, Feature Engineering, ML Model Evaluation |
| BAIL407 | Data Warehousing and Mining Laboratory | Lab | 1 | Data Cleaning and Integration, ETL Processes, OLAP Cube Operations, Apriori Algorithm Implementation, Decision Tree Construction |
| BIPH408 | Environmental Studies | Core | 0 | Ecosystems and Biodiversity, Environmental Pollution, Solid Waste Management, Sustainable Development, Environmental Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC501 | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| BCPC502 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing Strategies, Project Management |
| BAIC503 | Natural Language Processing | Core | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis |
| BPEP504 Example | Professional Elective – 1 (Example: Cloud Computing) | Elective | 3 | Cloud Computing Architectures, Service Models (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Security, Cloud Platforms (AWS/Azure/GCP Basics) |
| BPEPL505 Example | Professional Elective Lab – 1 (Example: Cloud Computing Lab) | Lab | 1 | Cloud Virtual Machine Deployment, Cloud Storage Management, Implementing Serverless Functions, Containerization with Docker, Basic Cloud Security Configurations |
| BAIL506 | Deep Learning Laboratory | Lab | 1 | Implementing Feedforward Networks, CNNs for Image Classification, RNNs for Sequence Prediction, Transfer Learning, Hyperparameter Tuning |
| BCSL507 | Software Engineering Laboratory | Lab | 1 | Agile Methodologies (Scrum), Version Control with Git, Requirements Gathering Tools, UML Diagrams, Software Testing Frameworks |
| BINT508 | Mini Project | Project | 2 | Problem Definition and Scope, Literature Survey, Project Design and Planning, Implementation and Testing, Report Writing and Presentation |
| BOEP509 Example | Open Elective – 1 (Example: Entrepreneurship) | Elective | 3 | Entrepreneurial Mindset, Business Plan Development, Startup Ecosystem, Marketing Strategies, Financial Management for Startups |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC601 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| BAIC602 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Extraction and Description, Object Detection, Image Segmentation, Deep Learning for Computer Vision |
| BAIC603 | Ethical AI and Trustworthy ML | Core | 3 | AI Ethics Principles, Bias and Fairness in ML, Explainable AI (XAI), Privacy Preserving AI, Responsible AI Development |
| BPEP604 Example | Professional Elective – 2 (Example: Big Data Analytics) | Elective | 3 | Big Data Concepts, Hadoop Ecosystem, Spark for Data Processing, NoSQL Databases, Data Visualization for Big Data |
| BPEPL605 Example | Professional Elective Lab – 2 (Example: Big Data Analytics Lab) | Lab | 1 | Hadoop HDFS Operations, MapReduce Programming, Spark RDDs and DataFrames, Cassandra/MongoDB Operations, Data Ingestion with Sqoop/Flume |
| BAIL606 | Reinforcement Learning Laboratory | Lab | 1 | Implementing Q-Learning, SARSA Algorithm, Policy Gradient Methods, OpenAI Gym Environment Interaction, Deep Q-Networks (DQNs) |
| BAIL607 | Computer Vision Laboratory | Lab | 1 | Image Loading and Manipulation with OpenCV, Edge Detection and Segmentation, Object Recognition using CNNs, Facial Recognition Techniques, Pose Estimation |
| BINT608 | Internship / Skill Development | Internship | 3 | Real-world Project Experience, Industry Best Practices, Professional Communication, Problem-Solving in a Team, Technical Documentation |
| BOEP609 Example | Open Elective – 2 (Example: Digital Marketing) | Elective | 3 | Search Engine Optimization (SEO), Social Media Marketing, Content Marketing, Email Marketing, Web Analytics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC701 | Artificial Neural Networks and Fuzzy Logic | Core | 4 | Perceptron and Backpropagation, Multi-Layer Perceptrons, Radial Basis Function Networks, Fuzzy Set Theory, Fuzzy Inference Systems |
| BAIC702 | AI in Robotics and Automation | Core | 4 | Robot Kinematics and Dynamics, Path Planning Algorithms, Robot Control Systems, Sensor Integration for Robotics, Human-Robot Interaction |
| BPEP703 Example | Professional Elective – 3 (Example: IoT for AI/ML) | Elective | 3 | IoT Architecture, Sensor Data Acquisition, Edge AI, Machine Learning on IoT Devices, IoT Security |
| BPEPL704 Example | Professional Elective Lab – 3 (Example: IoT for AI/ML Lab) | Lab | 1 | Setting up IoT Devices, Collecting Sensor Data, Deploying ML Models on Edge, Cloud Integration for IoT Data, Building Smart Applications |
| BAIL705 | Artificial Neural Networks and Fuzzy Logic Laboratory | Lab | 1 | Implementing ANN Architectures, Training Neural Networks, Fuzzy Logic Controller Design, Hybrid Neuro-Fuzzy Systems, Application of ANNs to classification/regression |
| BAIL706 | AI in Robotics and Automation Laboratory | Lab | 1 | Robot Programming Fundamentals, Kinematic Simulations, Path Planning Implementation, Sensor Integration with Robots, Robot Arm Control |
| BPROJ707 | Major Project – Phase 1 | Project | 4 | Problem Identification, Literature Review, Project Proposal Development, Design and Architecture, Initial Implementation |
| BOEP708 Example | Open Elective – 3 (Example: Research Methodology) | Elective | 3 | Research Problem Formulation, Data Collection Methods, Statistical Analysis, Report Writing, Ethical Considerations in Research |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BPEP801 Example | Professional Elective – 4 (Example: AI for Healthcare) | Elective | 3 | AI in Medical Imaging, Clinical Decision Support Systems, Drug Discovery with AI, Electronic Health Records Analysis, Predictive Analytics in Healthcare |
| BPEPL802 Example | Professional Elective Lab – 4 (Example: AI for Healthcare Lab) | Lab | 1 | Medical Image Processing with AI, Building Diagnostic Models, Wearable Sensor Data Analysis, Personalized Medicine Applications, Healthcare Data Privacy Practices |
| BPROJ803 | Major Project – Phase 2 | Project | 10 | Advanced Implementation and Integration, Rigorous Testing and Validation, Performance Optimization, Comprehensive Documentation, Project Presentation and Viva-Voce |
| BINT804 | Internship / Placement Training | Internship | 2 | Advanced Industry Project, Corporate Etiquette and Professionalism, Resume and Cover Letter Optimization, Technical and HR Interview Skills, Aptitude and Logical Reasoning Training |
| BOEP805 Example | Open Elective – 4 (Example: Intellectual Property Rights) | Elective | 3 | Types of IPR (Patents, Copyrights, Trademarks), IPR Laws in India, Infringement and Enforcement, Geographical Indications, IPR Management |
