.png&w=1920&q=75)
B-E in Artificial Intelligence Machine Learning at AMC Engineering College
Bengaluru, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence & Machine Learning at AMC Engineering College Bengaluru?
This Artificial Intelligence & Machine Learning program at AMC Engineering College equips students with a robust understanding of intelligent systems and data-driven decision-making. Essential for India''''s rapidly evolving tech landscape, it develops professionals who can innovate across sectors like healthcare, finance, and e-commerce. Blending theoretical foundations with practical applications, including deep learning, NLP, and big data, the program prepares students for complex real-world challenges, aligning with India''''s digital transformation.
Who Should Apply?
This program is ideal for analytically-minded fresh graduates with strong science and math backgrounds, aspiring to careers in AI and ML. It also suits working professionals, particularly from IT or engineering, seeking to upskill into advanced technical roles like AI Engineers or Data Scientists. Individuals passionate about solving problems through intelligent algorithms and keen on contributing to India''''s technological advancements, possessing a curiosity for data and automation, will find this curriculum highly rewarding.
Why Choose This Course?
Graduates can expect high-demand career paths across India, including Machine Learning Engineer, Data Scientist, AI Developer, or Business Intelligence Analyst. Entry-level salaries range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA in top Indian firms. The program fosters critical thinking, problem-solving, and practical implementation skills, enhancing employability. It also provides a strong foundation for higher studies or specialized AI certifications, contributing to India''''s skilled workforce in deep tech.
Student Success Practices
Foundation Stage
Master Programming & Data Structures- (Semester 1-2)
Dedicate significant time to mastering foundational programming languages (like C and Java) and data structures. Consistent coding practice, solving problems on platforms, and understanding algorithm efficiency are crucial for building core competency.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses on Data Structures
Career Connection
Strong programming skills are the bedrock for any AI/ML role, essential for writing efficient algorithms and implementing models in industry-standard languages, directly impacting entry-level technical roles.
Build Strong Mathematical Foundations- (Semester 1-2)
Focus on understanding the core concepts of Engineering Mathematics, Probability, and Statistics. These are fundamental for grasping complex AI/ML algorithms later. Form study groups to tackle challenging problems and clarify theoretical aspects.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL Mathematics lectures, Textbook exercises
Career Connection
A solid mathematical background is indispensable for understanding algorithm mechanics, optimizing models, and excelling in research-oriented AI/ML roles and data analysis positions.
Engage in Basic Project-Based Learning- (Semester 1-2)
Beyond lab assignments, try to build small, personal projects using basic programming concepts. This could be a simple calculator, a game, or a data organizer. Document your code and learning process rigorously.
Tools & Resources
GitHub for version control, VS Code/IDE, Online tutorials for beginner projects
Career Connection
Early project experience helps in developing problem-solving skills, debugging abilities, and creates a portfolio that demonstrates practical application, crucial for securing early internships and showcasing initiative.
Intermediate Stage
Deep Dive into AI/ML Core Concepts & Tools- (Semester 3-5)
Actively participate in Machine Learning and Deep Learning labs, experimenting with different algorithms and datasets. Become proficient in Python and relevant libraries like TensorFlow/PyTorch and Scikit-learn for practical implementation.
Tools & Resources
Kaggle for datasets and competitions, Coursera/Udemy specialized courses, Google Colab, Jupyter Notebooks
Career Connection
Hands-on expertise with industry-standard AI/ML tools and frameworks is directly applicable to Machine Learning Engineer and Data Scientist roles, making you job-ready for specialized positions.
Pursue Relevant Internships & Certifications- (Semester 4-5)
Actively seek out internships in AI/ML at startups or established companies in India to gain real-world exposure. Additionally, consider taking specialized certifications from platforms like NASSCOM FutureSkills Prime to validate your skills.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry certification platforms
Career Connection
Internships provide invaluable industry exposure, networking opportunities, and often lead to pre-placement offers. Certifications enhance your resume and demonstrate commitment, improving marketability in the Indian tech sector.
Network with Peers & Industry Professionals- (Semester 3-5)
Join AI/ML clubs or communities within the college or online. Attend webinars, workshops, and local tech meetups in Bengaluru. Network with seniors, professors, and industry professionals to gain insights and opportunities.
Tools & Resources
Local AI/ML communities, College events, LinkedIn for professional networking, Meetup.com
Career Connection
Networking opens doors to mentorship, collaborative projects, and job referrals, which are critical in the competitive Indian job market for identifying hidden opportunities and career growth.
Advanced Stage
Undertake Capstone Projects & Research- (Semester 7-8)
Focus on a significant final-year project that applies advanced AI/ML concepts to solve a real-world problem, potentially incorporating Generative AI or Reinforcement Learning. Consider publishing research papers in relevant conferences.
Tools & Resources
Access to high-performance computing, Industry mentors, Academic advisors, Research paper databases (e.g., IEEE, ACM)
Career Connection
A strong capstone project is a key differentiator in placements, showcasing deep expertise. Research experience is vital for R&D roles, higher studies, or positions requiring innovation.
Specialize through Electives & Advanced Skills- (Semester 6-8)
Carefully choose professional and open electives to align with your career aspirations (e.g., Computer Vision, NLP, Edge AI). Develop expertise in niche areas and emerging technologies highly relevant to India''''s tech growth.
Tools & Resources
Advanced online courses (e.g., edX, Udacity), Specialized workshops, Industry-specific hackathons
Career Connection
Specialization makes you a valuable asset for specific industry roles, improving placement prospects and allowing you to command higher salaries in your chosen domain within the Indian job market.
Prepare for Placements and Professional Development- (Semester 7-8)
Engage in rigorous placement preparation, including mock interviews, aptitude tests, and resume building workshops. Practice articulating your project work and technical skills effectively. Refine soft skills for corporate readiness and professional conduct.
Tools & Resources
College placement cell, Career counseling services, Interview preparation platforms (e.g., InterviewBit, Pramp), Communication skill workshops
Career Connection
Effective preparation maximizes your chances of securing desirable job offers from top companies in India''''s booming AI/ML sector, leading to a successful career launch.
Program Structure and Curriculum
Eligibility:
- As per Karnataka State and AICTE norms; typically, 10+2 with Physics, Mathematics, and one of Chemistry/Biology/Biotechnology/Electronics, along with a valid entrance examination (KCET/JEE) score.
Duration: 8 semesters / 4 years
Credits: 171 (as calculated from individual subjects, official document states 160) Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BSC101 | Engineering Mathematics-I | Basic Science Course | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Differential Equations |
| 23PCD102 | Programming for Problem Solving | Professional Core Course | 4 | Introduction to C, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures |
| 23BEE103 | Basic Electrical & Electronics Engineering | Basic Engineering Course | 3 | DC & AC Circuits, Electrical Machines, Diodes & Transistors, Operational Amplifiers, Digital Electronics Fundamentals |
| 23MEP104 | Elements of Mechanical Engineering | Basic Engineering Course | 3 | Thermodynamics, Fluid Mechanics, IC Engines, Power Transmission, Material Science |
| 23ACH105 | Engineering Chemistry | Basic Science Course | 3 | Electrochemistry, Polymer Chemistry, Corrosion, Water Chemistry, Material Characterization |
| 23HSL106 | Communicative English | Humanities & Social Sciences | 2 | Basic Communication Skills, Public Speaking, Technical Writing, Vocabulary and Grammar, Presentation Skills |
| 23PCDL107 | Programming for Problem Solving Laboratory | Lab Course | 1 | C Programming Basics, Conditional Statements, Looping Constructs, Arrays and Functions, File Operations |
| 23CADL108 | Engineering Graphics & Computer Aided Drafting Lab | Lab Course | 1 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics, Solid Modeling |
| 23AEX109 | Engineering Chemistry Laboratory | Lab Course | 1 | Chemical Analysis, Water Quality Testing, Synthesis of Polymers, Spectrophotometry, pH Metry |
| 23KVS110 | Kannada / Sanskrit | Mandatory Non-Credit Course | 0 | Basic Kannada/Sanskrit, Grammar Fundamentals, Conversational Skills, Cultural Significance, Reading and Writing |
| 23CIP111 | Computer & Information Processing Lab | Skill Development Course | 1 | OS Fundamentals, MS Office Suite, Internet Browsing, Basic Networking, Data Management |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BSC201 | Engineering Mathematics-II | Basic Science Course | 4 | Linear Algebra, Laplace Transforms, Fourier Series, Complex Analysis, Probability & Statistics |
| 23CPC202 | Data Structures | Professional Core Course | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Hashing Techniques, Sorting and Searching Algorithms |
| 23BEE203 | Elements of Civil Engineering | Basic Engineering Course | 3 | Surveying Principles, Building Materials, Structural Systems, Water Resources Engineering, Transportation Engineering |
| 23MEP204 | Elements of Electrical & Electronics Engineering | Basic Engineering Course | 3 | Electrical Circuits, Measuring Instruments, Semiconductor Devices, Digital Logic Gates, Communication Systems Overview |
| 23APH205 | Engineering Physics | Basic Science Course | 3 | Quantum Mechanics, Solid State Physics, Lasers and Applications, Optical Fibers, Nanotechnology |
| 23HSL206 | Professional Ethics & Indian Constitution | Humanities & Social Sciences | 2 | Professional Ethics, Cyber Ethics, Indian Constitution, Human Rights, Environmental Laws |
| 23CPCL207 | Data Structures Laboratory | Lab Course | 1 | Array Operations, Linked List Implementation, Stack & Queue Applications, Tree Traversal Algorithms, Graph Algorithms |
| 23AEL208 | Engineering Physics Laboratory | Lab Course | 1 | Experiments on Lasers, Optical Fiber Characteristics, Semiconductor Devices, Magnetic Field Measurements, Crystal Structure Analysis |
| 23MECL209 | Mechanical Engineering Workshop | Lab Course | 1 | Benchwork and Fitting, Welding Techniques, Machining Operations, Carpentry Skills, Sheet Metal Work |
| 23CIP210 | Computer & Information Processing | Skill Development Course | 1 | Advanced Spreadsheet Skills, Presentation Tools, Database Management Basics, Cloud Computing Introduction, Internet Security Concepts |
| 23EVL211 | Environmental Science | Mandatory Non-Credit Course | 0 | Ecosystems and Biodiversity, Environmental Pollution, Renewable Energy Sources, Environmental Management, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA301 | Transform Calculus, Fourier Series and Numerical Techniques | Basic Science Course | 3 | Laplace Transforms, Fourier Series, Z-Transforms, Numerical Methods, Finite Differences |
| 23AIM302 | Discrete Mathematical Structures | Professional Core Course | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| 23AIM303 | Analog and Digital Electronics | Professional Core Course | 4 | Semiconductor Devices, Amplifiers and Oscillators, Operational Amplifiers, Digital Logic Gates, Combinational & Sequential Circuits |
| 23AIM304 | Object Oriented Programming with JAVA | Professional Core Course | 4 | OOP Concepts, Java Syntax and Features, Inheritance and Polymorphism, Exception Handling, Collections Framework |
| 23AIM305 | Database Management Systems | Professional Core Course | 4 | Database Concepts, ER Model and Relational Model, SQL Query Language, Normalization, Transaction Management |
| 23AIML306 | Introduction to Artificial Intelligence | Professional Core Course | 3 | AI Agents and Problem Solving, Search Algorithms, Knowledge Representation, Machine Learning Overview, Expert Systems |
| 23INT307 | Industry Internship-I | Internship | 1 | Project Work, Industry Exposure, Professional Skill Development, Report Writing, Presentation Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA401 | Probability and Queuing Theory | Basic Science Course | 3 | Probability Distributions, Random Variables, Stochastic Processes, Queuing Models, Markov Chains |
| 23AIM402 | Design and Analysis of Algorithms | Professional Core Course | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and NP-Completeness |
| 23AIM403 | Microcontrollers and Embedded Systems | Professional Core Course | 4 | Microcontroller Architecture, Embedded C Programming, Interfacing Techniques, Embedded System Design, RTOS Concepts and IoT Devices |
| 23AIM404 | Operating Systems | Professional Core Course | 4 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File Systems and I/O Systems |
| 23AIM405 | Machine Learning | Professional Core Course | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Model Evaluation and Neural Networks |
| 23AIM406 | Object Oriented Programming with Python | Professional Core Course | 3 | Python Fundamentals, OOP in Python, Data Structures in Python, File Handling and Exceptions, Modules and Packages |
| 23RMC407 | Research Methodology & IPR | Mandatory Non-Credit Course | 0 | Research Design, Data Collection and Analysis, Report Writing, IPR Basics, Patents and Copyrights |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM501 | Computer Networks | Professional Core Course | 4 | OSI/TCP-IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer and Security |
| 23AIM502 | Artificial Neural Networks and Deep Learning | Professional Core Course | 4 | Perceptrons and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and Autoencoders, Deep Learning Frameworks |
| 23AIM503 | Data Warehousing and Data Mining | Professional Core Course | 4 | Data Warehousing Concepts, OLAP and ETL, Data Preprocessing, Association Rule Mining, Classification and Clustering |
| 23AIMPE5041 | Web Technologies | Professional Elective | 3 | HTML, CSS, JavaScript, Server-side Scripting, Web Frameworks, Database Connectivity, Web Security Basics |
| 23AIMPE5042 | Advanced Data Structures & Algorithms | Professional Elective | 3 | Advanced Trees, Heaps and Hash Tables, Graph Algorithms, String Matching Algorithms, Amortized Analysis |
| 23AIMPE5043 | Cyber Security | Professional Elective | 3 | Network Security, Cryptography, Firewalls and IDS/IPS, Malware Analysis, Ethical Hacking Fundamentals |
| 23AIMPE5044 | Business Intelligence | Professional Elective | 3 | BI Concepts, Data Integration, Reporting and Dashboards, Data Visualization, Decision Support Systems |
| 23AIMOE5051 | Data Communication | Open Elective | 3 | Analog/Digital Transmission, Data Encoding, Multiplexing Techniques, Error Detection and Correction, Switching Technologies |
| 23AIMOE5052 | Software Engineering | Open Elective | 3 | Software Life Cycle Models, Requirements Engineering, Design Principles, Software Testing, Project Management |
| 23AIMOE5053 | Operation Research | Open Elective | 3 | Linear Programming, Simplex Method, Transportation Problems, Assignment Problems, Network Models |
| 23AIMOE5054 | Cloud Computing | Open Elective | 3 | Cloud Models (IaaS, PaaS, SaaS), Virtualization, Cloud Security, Cloud Storage, Distributed Systems Concepts |
| 23AIMINT506 | Internship-II | Internship | 2 | Project Implementation, Industry Best Practices, Teamwork and Collaboration, Communication Skills, Professional Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM601 | Natural Language Processing | Professional Core Course | 4 | Text Preprocessing, NLP Tasks, Language Models, Word Embeddings, Recurrent Neural Networks and Transformers |
| 23AIM602 | Big Data Analytics | Professional Core Course | 4 | Big Data Ecosystem, Hadoop and MapReduce, Spark Framework, NoSQL Databases, Data Visualization and Stream Processing |
| 23AIM603 | Computer Vision | Professional Core Course | 4 | Image Fundamentals, Feature Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| 23AIMPE6041 | Full Stack Development | Professional Elective | 3 | Frontend Frameworks, Backend Development, REST APIs, Database Integration, Application Deployment |
| 23AIMPE6042 | Distributed Computing | Professional Elective | 3 | Distributed Systems Concepts, IPC and RPC, Consensus Protocols, Distributed File Systems, Cloud Computing Basics |
| 23AIMPE6043 | Block Chain Technology | Professional Elective | 3 | Cryptography Fundamentals, Distributed Ledgers, Blockchain Architecture, Smart Contracts, Decentralized Applications (DApps) |
| 23AIMPE6044 | Information Retrieval | Professional Elective | 3 | IR Models, Indexing and Query Processing, Ranking Algorithms, Text Classification, Web Search Technologies |
| 23AIMOE6051 | Digital Image Processing | Open Elective | 3 | Image Enhancement, Image Restoration, Image Compression, Image Segmentation, Morphological Operations |
| 23AIMOE6052 | Data Visualization | Open Elective | 3 | Visualization Principles, Chart Types, Tools (Tableau/PowerBI), Interactive Dashboards, Storytelling with Data |
| 23AIMOE6053 | Management and Entrepreneurship | Open Elective | 3 | Management Functions, Organizational Structure, Entrepreneurship, Business Plan Development, Startup Ecosystem |
| 23AIMOE6054 | Internet of Things | Open Elective | 3 | IoT Architecture, Sensors & Actuators, Communication Protocols, IoT Platforms, IoT Security and Applications |
| 23AIMIE606 | Industry Exposure | Internship/Project | 2 | Industrial Visits, Mini Projects, Case Studies, Technical Discussions, Mentorship and Networking |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIM701 | Reinforcement Learning | Professional Core Course | 4 | Markov Decision Processes, Value and Policy Iteration, Q-Learning, Deep Reinforcement Learning, Applications of RL |
| 23AIM702 | Generative AI | Professional Core Course | 4 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, Transformers for Generation, Ethical AI Considerations |
| 23AIMPE7031 | Human Computer Interaction | Professional Elective | 3 | HCI Principles, Usability Engineering, User-Centered Design, Interface Prototyping, Evaluation Methods |
| 23AIMPE7032 | Quantum Computing | Professional Elective | 3 | Quantum Mechanics Basics, Qubits and Quantum Gates, Quantum Algorithms, Quantum Cryptography, Quantum Computing Platforms |
| 23AIMPE7033 | Edge Computing | Professional Elective | 3 | Edge Architecture, Fog Computing, Distributed AI at Edge, Latency Optimization, Security at the Edge |
| 23AIMPE7034 | Speech and Audio Processing | Professional Elective | 3 | Speech Production, Signal Processing for Audio, Feature Extraction, Speech Recognition, Speaker Identification |
| 23AIMOE7041 | Data Science for Engineers | Open Elective | 3 | Data Science Workflow, Statistical Modeling, Machine Learning Algorithms, Data Visualization, Case Studies |
| 23AIMOE7042 | Introduction to Robotics | Open Elective | 3 | Robot Kinematics and Dynamics, Actuators and Sensors, Robot Control, AI in Robotics, Robotic Applications |
| 23AIMOE7043 | Wireless Sensor Networks | Open Elective | 3 | WSN Architecture, Protocols and Routing, Localization Techniques, WSN Security, Applications of WSN |
| 23AIMOE7044 | Project Management | Open Elective | 3 | Project Life Cycle, Planning and Scheduling, Resource Management, Risk Management, Agile Methodologies |
| 23AIMPRJ705 | Project Work Phase - I | Project | 4 | Problem Identification, Literature Survey, System Design, Methodology Development, Initial Implementation |
| 23AIMINT706 | Internship / Industrial Training | Internship | 4 | Real-world Project Experience, Industry Tools and Technologies, Professional Communication, Technical Documentation, Workplace Ethics |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIMPE8011 | Fuzzy Logic & Genetic Algorithms | Professional Elective | 3 | Fuzzy Sets and Logic, Fuzzy Inference Systems, Genetic Algorithms, Optimization Techniques, Hybrid Systems |
| 23AIMPE8012 | IoT and AI | Professional Elective | 3 | IoT Architecture, Edge AI for IoT, Cloud AI for IoT, Data Analytics on IoT, Security in IoT Systems |
| 23AIMPE8013 | Ethics in AI | Professional Elective | 3 | AI Ethics Principles, Bias in AI, Privacy and Data Protection, Transparency and Accountability, Societal Impact of AI |
| 23AIMPE8014 | Robotic Process Automation | Professional Elective | 3 | RPA Concepts, Process Mapping, RPA Tools (e.g., UiPath, Automation Anywhere), Bots Development, Deployment and Use Cases |
| 23AIMOE8021 | Human Rights and Cyber Laws | Open Elective | 3 | Human Rights Principles, Cyber Laws in India, IT Act and Amendments, Data Privacy and Protection, Cybercrime and Digital Forensics |
| 23AIMOE8022 | Additive Manufacturing | Open Elective | 3 | 3D Printing Technologies, Materials for Additive Manufacturing, Design for Additive Manufacturing, Post-processing Techniques, Applications of Additive Manufacturing |
| 23AIMOE8023 | Sustainable Engineering | Open Elective | 3 | Environmental Impact Assessment, Green Technologies, Life Cycle Assessment, Renewable Energy Systems, Eco-design Principles |
| 23AIMOE8024 | System Modelling and Simulation | Open Elective | 3 | System Dynamics, Simulation Techniques, Discrete Event Simulation, Monte Carlo Simulation, Model Validation |
| 23AIMPRJ803 | Project Work Phase - II | Project | 10 | Full System Implementation, Testing and Validation, Results Analysis, Thesis Writing, Project Defense |
| 23AIMSE804 | Technical Seminar | Seminar | 1 | Technical Presentation Skills, Literature Review, Current Technologies, Communication Skills, Subject Matter Expertise |
| 23AIMCE805 | Comprehensive Viva Voce | Viva Voce | 2 | Overall Engineering Concepts, Specialization Knowledge, Interview Skills, Problem Solving Aptitude, Technical Communication |
