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B-E in Artificial Intelligence Machine Learning at Cambridge Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Cambridge Institute of Technology Bengaluru?
This Artificial Intelligence & Machine Learning (AIML) program at Cambridge Institute of Technology focuses on equipping students with a robust foundation in cutting-edge AI and ML methodologies. In the rapidly evolving Indian tech landscape, this specialization is crucial for developing intelligent systems, from automated processes to predictive analytics. The program distinguishes itself by combining theoretical depth with practical, industry-aligned applications, preparing graduates for the demands of a data-driven economy.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, programming, and problem-solving, seeking entry into the dynamic fields of AI and ML. It also caters to working professionals in software development or data analysis looking to upskill and transition into specialized AI roles. Career changers with a science or engineering background aiming to pivot into this high-growth industry will find the curriculum comprehensive and supportive, leveraging their analytical abilities.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Scientist, Data Scientist, NLP Engineer, and Computer Vision Specialist. Entry-level salaries typically range from INR 4-8 LPA, growing significantly to INR 15-30+ LPA with experience in leading Indian and global firms. The program''''s strong curriculum aligns with professional certifications like Google AI, AWS ML, and NVIDIA DLI, enhancing career growth trajectories in the burgeoning Indian AI sector.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice core programming concepts (C, Python, Data Structures) beyond classroom assignments. Utilize online coding platforms to solve diverse problems, focusing on logic building and algorithm efficiency.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, Python documentation
Career Connection
Strong coding skills are fundamental for entry-level AI/ML roles and crucial for clearing technical interviews and practical challenges during placements.
Build a Strong Mathematical Base- (Semester 1-2)
Pay close attention to Engineering Mathematics, especially Linear Algebra, Calculus, and Probability. These form the bedrock of AI/ML algorithms. Form study groups to tackle complex problems and explore applications of these concepts in real-world scenarios.
Tools & Resources
Khan Academy, NPTEL courses on Mathematics, Wolfram Alpha
Career Connection
A deep understanding of mathematical principles is essential for comprehending, optimizing, and developing advanced AI/ML models, setting a strong foundation for research and development roles.
Engage in Early Project Exploration- (Semester 1-2)
Start experimenting with mini-projects using Python and basic libraries like NumPy and Pandas. Focus on data manipulation and simple algorithms. Participate in college tech clubs or workshops to gain hands-on experience and collaborate with peers.
Tools & Resources
Kaggle for datasets, Jupyter Notebooks, Google Colab, GitHub for version control
Career Connection
Early project exposure builds a practical portfolio, demonstrates initiative, and helps in identifying areas of interest within AI/ML, which is vital for internships and future specialization.
Intermediate Stage
Specialize in Core AI/ML Domains- (Semester 3-5)
Deep dive into core AI/ML concepts like Supervised/Unsupervised Learning, Deep Learning, and NLP. Beyond coursework, pursue online certifications and MOOCs from platforms like Coursera/edX to gain specialized knowledge and practical skills.
Tools & Resources
Coursera (Deep Learning Specialization by Andrew Ng), edX (Microsoft/IBM AI courses), TensorFlow/PyTorch documentation, Hugging Face
Career Connection
Specialization makes candidates highly desirable for specific AI/ML roles (e.g., NLP Engineer, Computer Vision Scientist) and provides a competitive edge during campus placements.
Participate in Hackathons & Competitions- (Semester 3-5)
Actively join AI/ML focused hackathons (e.g., Smart India Hackathon, local college fests) and online competitions (Kaggle). This provides hands-on problem-solving experience, teamwork skills, and exposure to real-world datasets and challenges.
Tools & Resources
Kaggle, Devpost, Google AI Competitions
Career Connection
Winning or even participating builds a strong resume, demonstrates practical application skills, and offers networking opportunities with industry professionals and recruiters.
Seek Industry Internships- (Semester 4-5)
Actively search for and complete internships in AI/ML roles at startups, mid-sized companies, or MNCs. Even short-term internships provide invaluable industry exposure, mentorship, and a chance to apply academic knowledge to real business problems.
Tools & Resources
LinkedIn, Internshala, Company career pages, College placement cell
Career Connection
Internships are often a direct pathway to pre-placement offers, provide industry references, and significantly enhance employability by showcasing practical experience.
Advanced Stage
Develop a Capstone Project & Portfolio- (Semester 6-8)
Dedicate significant effort to the final year project, aiming for an innovative and impactful solution to a real-world problem using advanced AI/ML techniques. Document the project thoroughly and build a professional online portfolio (GitHub, personal website).
Tools & Resources
GitHub, Google Cloud/AWS Free Tier, Research papers
Career Connection
A strong, well-documented project and portfolio are critical for showcasing capabilities to potential employers, especially for R&D or specialized AI roles.
Focus on Placement-Specific Skill Enhancement- (Semester 6-8)
Alongside advanced studies, prepare intensively for placements by practicing aptitude tests, mock interviews (technical and HR), and group discussions. Refine resume and communication skills. Network with alumni and industry professionals for insights.
Tools & Resources
Placement preparation books (R.S. Aggarwal), Online interview platforms (Pramp, InterviewBit), LinkedIn
Career Connection
Targeted preparation significantly increases the chances of securing desired placements in top-tier companies, maximizing career opportunities right after graduation.
Explore Advanced Electives & Research- (Semester 7-8)
Select professional electives that align with desired career paths (e.g., IoT & Edge AI, Quantum Computing, Generative AI). Consider publishing research papers or presenting at conferences, especially if interested in higher studies or R&D roles.
Tools & Resources
IEEE Xplore, ArXiv, Scopus, Relevant academic conferences
Career Connection
Specializing through advanced electives and engaging in research positions graduates for cutting-edge roles, fosters critical thinking, and opens doors to academic pursuits or highly specialized industry positions.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry / Biotechnology / Biology / Technical Vocational subject. Obtained at least 45% marks (40% in case of candidate belonging to reserved category) in the above subjects taken together. Admission through KCET/COMEDK/Management Quota.
Duration: 8 semesters / 4 years
Credits: 148 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMATS101 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| BPHYT102 | Engineering Physics | Core | 3 | Quantum Mechanics, Lasers, Optical Fibers, Dielectric Materials, Superconductivity |
| BBETL103 | Basic Electrical & Electronics Engineering | Core | 3 | DC & AC Circuits, Electrical Machines, Diodes & Transistors, Operational Amplifiers |
| BCSL104 | Programming for Problem Solving | Core | 3 | C Fundamentals, Control Structures, Functions, Arrays and Pointers, Structures and File Handling |
| BSDG105 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces |
| BHUT106 | Professional Communication | Core | 1 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Presentation Skills |
| BPHYL107 | Engineering Physics Laboratory | Lab | 1 | Light Interference & Diffraction, Electrical Conductivity, Semiconductor Devices |
| BBETL108 | Basic Electrical & Electronics Engineering Laboratory | Lab | 1 | Circuit Laws Verification, Diode Characteristics, Transistor Amplifier Circuits |
| BPHT109 | Constitution of India & Professional Ethics | Audit | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber Laws |
| BHUT110 | Samskruthika Kannada | Audit | 0 | Kannada Language Skills, Karnataka Culture, Literary Appreciation |
| BHUT111 | Balake Kannada | Audit | 0 | Spoken Kannada Basics, Everyday Conversations, Kannada Script |
| BHSK112 | Holistic Education / NSS / NSO / Yoga | Audit | 0 | Personality Development, Community Service, Physical Fitness, Mental Well-being |
| BSDK113 | Scientific Foundations for Engineering | Audit | 0 | Scientific Method, Physical Quantities, Material Science Basics, Environmental Concepts |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMATS201 | Engineering Mathematics-II | Core | 4 | Linear Algebra, Multiple Integrals, Vector Integration, Numerical Methods, Probability and Statistics |
| BCYTS202 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion and its Control, Fuels and Batteries, Polymers |
| BCSL203 | C Programming and Data Structures | Core | 3 | Arrays and Strings, Stacks and Queues, Linked Lists, Trees and Graphs, Sorting and Searching |
| BMETL204 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Material Science |
| BBEL205 | Basic Electronics | Core | 3 | Diode Applications, Transistor Biasing, Operational Amplifiers, Digital Logic Gates, Communication Systems |
| BCYL206 | Engineering Chemistry Laboratory | Lab | 1 | Water Quality Analysis, pH and Conductivity Metry, Colorimetry Experiments |
| BCSL207 | C Programming and Data Structures Laboratory | Lab | 1 | Stack and Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Sorting and Searching Algorithms |
| BMEL208 | Computer Aided Engineering Drawing | Lab | 1 | Orthographic Projections using CAD, Isometric Views using CAD, Sectional Views in CAD |
| BCHT209 | Constitution of India, Professional Ethics & Human Rights | Audit | 0 | Indian Constitution, Human Rights, Professional Ethics, Cyber Laws |
| BEMT210 | Environmental Studies | Audit | 0 | Ecosystems, Environmental Pollution, Renewable Energy, Waste Management |
| BENT211 | Entrepreneurship and Innovation | Audit | 0 | Entrepreneurship Concepts, Business Plan Development, Startup Ecosystem, Innovation Strategies |
| BPL212 | Python Programming | Core | 3 | Python Basics, Data Types & Structures, Control Flow, Functions and Modules, Object-Oriented Programming |
| BPL213 | Python Programming Laboratory | Lab | 1 | Conditional Statements & Loops, Function Implementation, File Handling, Module Usage, Data Structure Manipulation |
| BSKK214 | Samskruthika Kannada | Audit | 0 | Kannada Language Skills, Karnataka Culture, Literary Appreciation |
| BKK215 | Balake Kannada | Audit | 0 | Spoken Kannada Basics, Everyday Conversations, Kannada Script |
| BHSH216 | Holistic Education / NSS / NSO / Yoga | Audit | 0 | Personality Development, Community Service, Physical Fitness, Mental Well-being |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML301 | Data Structures | Core | 4 | Stacks and Queues, Linked Lists, Trees and Heaps, Graphs, Hashing and Sorting |
| 21AIML302 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Algebraic Structures, Combinatorics and Probability |
| 21AIML303 | Analog & Digital Electronics | Core | 3 | Diode Circuits, Transistor Amplifiers, Operational Amplifiers, Logic Gates and Boolean Algebra, Combinational & Sequential Circuits |
| 21AIML304 | Computer Organization & Architecture | Core | 3 | CPU Design, Memory Organization, I/O Organization, Instruction Sets, Pipelining and Parallel Processing |
| 21AIML305 | Artificial Intelligence | Core | 3 | AI Fundamentals, Problem Solving by Search, Knowledge Representation, AI Planning, Introduction to Machine Learning |
| 21AIML306 | Data Structures Laboratory | Lab | 1 | Implementation of Stacks, Queues, Linked List Operations, Binary Search Tree Traversal, Graph Algorithms |
| 21AIML307 | Analog & Digital Electronics Laboratory | Lab | 1 | Digital IC Experiments, Analog Circuit Characteristics, Combinational Logic Design, Sequential Logic Implementation |
| 21AIML308 | AI & ML Workshop | Lab | 1 | Python for AI/ML, Numpy and Pandas Basics, Data Preprocessing, Basic ML Model Implementation |
| 21CIP309 | Civil Engineering & Environmental Science | Audit | 0 | Building Materials, Surveying Basics, Water Resources, Air and Noise Pollution, Waste Management |
| 21CPL310 | Communicative English | Audit | 0 | Communication Theory, Public Speaking, Group Discussion, Technical Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML401 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, NP-Completeness |
| 21AIML402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 21AIML403 | Database Management Systems | Core | 3 | Data Models, SQL and Relational Algebra, ER Modeling, Normalization, Transaction Management |
| 21AIML404 | Microcontroller | Core | 3 | 8051 Architecture, Instruction Set, Assembly Language Programming, Interfacing Peripherals, Timers and Interrupts |
| 21AIML405 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation and Optimization |
| 21AIML406 | Operating Systems Laboratory | Lab | 1 | Shell Scripting, Process and Thread Management, System Calls, Memory Allocation Algorithms |
| 21AIML407 | Database Management Systems Laboratory | Lab | 1 | SQL Querying, PL/SQL Programming, Database Design, Transaction Control |
| 21AIML408 | Microcontroller Laboratory | Lab | 1 | 8051 Assembly Programming, C Programming for 8051, Interfacing with LEDs, LCDs, Sensor Integration |
| 21FOC409 | Foundation for Outcome-Based Education | Audit | 0 | OBE Principles, Bloom''''s Taxonomy, Course Outcomes, Program Outcomes |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML501 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Agile Methodologies |
| 21AIML502 | Computer Networks | Core | 4 | OSI/TCP-IP Models, Data Link Layer, Network Layer Protocols, Transport Layer, Application Layer Services |
| 21AIML503 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Optimization and Regularization |
| 21AIML504 | Professional Elective - I | Elective | 3 | Specific topics based on chosen elective (e.g., NLP, Computer Vision, Reinforcement Learning, Data Warehousing & Data Mining) |
| 21AIML505 | Open Elective - I | Open Elective | 3 | Topics vary based on offering department (e.g., Marketing, Finance, Industrial Safety, etc.) |
| 21AIML506 | Computer Networks Laboratory | Lab | 1 | Network Configuration, Socket Programming, Protocol Analysis, Network Security Tools |
| 21AIML507 | Deep Learning Laboratory | Lab | 1 | CNN Implementation with TensorFlow, RNN Model Development, Image Classification, Sequence Generation |
| 21AIML508 | Project Work Phase - I / Internship | Core | 2 | Problem Identification, Literature Survey, Feasibility Study, Initial Design |
| 21AIML509 | Research Methodology & IPR | Audit | 0 | Research Process, Data Collection & Analysis, Patents and Copyrights, Intellectual Property Rights |
| 21AIPL510 | Innovation and Design Thinking | Audit | 0 | Design Thinking Process, Ideation Techniques, Prototyping, User-Centric Design |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML601 | Big Data Analytics | Core | 4 | Hadoop Ecosystem, MapReduce Framework, Spark Programming, NoSQL Databases, Data Streaming |
| 21AIML602 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Security, AWS/Azure/GCP Services, Serverless Computing |
| 21AIML603 | Professional Elective - II | Elective | 3 | Specific topics based on chosen elective (e.g., Robotics, Ethical AI, Speech Processing, Generative AI) |
| 21AIML604 | Open Elective - II | Open Elective | 3 | Topics vary based on offering department (e.g., Entrepreneurship, Supply Chain Management, Cybersecurity Basics) |
| 21AIML605 | Big Data Analytics Laboratory | Lab | 1 | Hadoop Installation & Configuration, MapReduce Programs, Spark Data Processing, Hive and Pig Queries |
| 21AIML606 | Cloud Computing Laboratory | Lab | 1 | Virtual Machine Deployment, Cloud Storage Services, Web Application Hosting, Containerization with Docker |
| 21AIML607 | Project Work Phase - II | Core | 2 | System Design, Module Implementation, Interim Testing, Documentation |
| 22AIML608 | Mini Project | Core | 2 | Problem Scoping, Mini Project Design, Implementation of Solution, Report Writing |
| 21AIML609 | Constitution of India and Professional Ethics | Audit | 0 | Indian Constitution, Fundamental Duties, Ethical Dilemmas in Engineering, Corporate Governance |
| 21AIML610 | Universal Human Values | Audit | 0 | Self-Exploration, Harmony in Society, Ethical Conduct, Professional Ethics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML701 | Professional Elective - III | Elective | 3 | Specific topics based on chosen elective (e.g., IoT & Edge AI, Human Computer Interaction, Data Visualization, Quantum Computing) |
| 21AIML702 | Professional Elective - IV | Elective | 3 | Specific topics based on chosen elective (e.g., Augmented & Virtual Reality, Blockchain Technology, Information Retrieval, Game Theory) |
| 21AIML703 | Project Work Phase - III | Core | 6 | Advanced Module Implementation, Testing and Validation, Result Analysis, Interim Project Report |
| 21AIML704 | Internship | Core | 3 | Industry Experience, Application of Skills, Problem Solving, Internship Report & Presentation |
| 21AIML705 | Technical Seminar | Core | 1 | Literature Review, Technical Presentation Skills, Advanced Research Topics, Q&A and Discussion |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML801 | Professional Elective - V | Elective | 3 | Specific topics based on chosen elective (e.g., GPU Computing, Conversational AI, Explainable AI, Federated Learning) |
| 21AIML802 | Project Work Phase - IV | Core | 10 | Final System Integration, Performance Evaluation, Thesis Writing, Viva-Voce Examination |
| 21AIML803 | Internship / Technical Skill Development | Core | 3 | Advanced Industry Training, Specialized Skill Acquisition, Professional Certification Preparation, Real-world Project Deployment |
| 21AIML804 | Technical Seminar | Core | 1 | Advanced Research Presentation, Project Outcome Dissemination, Technical Communication, Future Scope Discussions |
