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B-TECH in Artificial Intelligence at Amity University, Kolkata
Kolkata, West Bengal
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About the Specialization
What is Artificial Intelligence at Amity University, Kolkata Kolkata?
This Artificial Intelligence program at Amity University, North 24 Parganas, West Bengal, focuses on equipping students with advanced knowledge and skills in intelligent systems. It covers core AI concepts, machine learning, deep learning, and their applications, aligning with the growing demand for AI professionals across various Indian industries. The program emphasizes a blend of theoretical foundations and practical implementation for real-world problem-solving.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and computer science, seeking entry into the rapidly expanding AI sector in India. It also caters to working professionals aiming to upskill in AI/ML for career advancement, and career changers transitioning into data science or AI roles. A prerequisite background in basic programming and mathematics is beneficial.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as AI Engineers, Machine Learning Scientists, Data Scientists, and Robotics Engineers. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The program prepares students for industry certifications and leadership roles in Indian tech giants and innovative startups.
Student Success Practices
Foundation Stage
Master Programming & Data Structures- (Semester 1-2)
Focus rigorously on C/C++ and Python programming fundamentals, alongside data structures and algorithms. Participate in coding competitions on platforms like CodeChef and HackerRank to hone problem-solving skills.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, LeetCode, C/C++ textbooks, Python programming tutorials
Career Connection
Strong programming and DSA skills are fundamental for entry-level roles in software development and are crucial for AI/ML algorithm implementation. Essential for technical interviews.
Build a Strong Mathematical Foundation- (Semester 1-2)
Pay close attention to Engineering Mathematics, Discrete Mathematics, Probability, and Statistics. Understand the underlying mathematical principles as they are critical for grasping advanced AI and Machine Learning concepts. Seek supplementary resources and practice problems.
Tools & Resources
NPTEL courses on Mathematics, Khan Academy, textbook exercises
Career Connection
A robust mathematical background is indispensable for understanding complex AI algorithms, performing research, and developing novel AI solutions.
Engage in Early Project-Based Learning- (Semester 1-2)
Beyond coursework, start building small projects, even simple ones, using basic programming and data structures. This helps in practical application of learned concepts and builds a portfolio. Collaborate with peers on mini-projects.
Tools & Resources
GitHub, VS Code, open-source project ideas, online tutorials
Career Connection
Early project experience demonstrates initiative and practical skills, making students more attractive to recruiters for internships and full-time roles.
Intermediate Stage
Deep Dive into AI/ML Frameworks and Libraries- (Semester 3-5)
Actively learn and implement concepts from Artificial Intelligence, Machine Learning, and Deep Learning using industry-standard libraries like TensorFlow, PyTorch, and Scikit-learn. Work on practical projects to build proficiency.
Tools & Resources
TensorFlow tutorials, PyTorch documentation, Scikit-learn examples, Kaggle datasets, Coursera/Udemy specialized courses
Career Connection
Proficiency in these tools is directly applicable to AI/ML engineering roles, data science positions, and helps in building a strong project portfolio for placements.
Seek Internships and Industry Mentorship- (Semester 4-5)
Actively pursue internships in AI/ML roles at startups, tech companies, or research institutions. Network with professionals through LinkedIn and industry events to gain mentorship and insights into real-world applications of AI.
Tools & Resources
LinkedIn, Internshala, college career services, industry meetups and conferences
Career Connection
Internships provide invaluable practical experience, professional networking opportunities, and often lead to pre-placement offers, significantly boosting career prospects.
Participate in AI/Data Science Competitions- (Semester 4-5)
Engage in online competitions on platforms like Kaggle, DrivenData, or participate in hackathons focusing on AI and data science challenges. This helps apply learned concepts to complex problems and enhances resume value.
Tools & Resources
Kaggle, DrivenData, Analytics Vidhya, GitHub for open-source contributions
Career Connection
Success in competitions demonstrates advanced problem-solving abilities and practical AI skills, making candidates stand out in the competitive job market.
Advanced Stage
Develop a Capstone AI Project with Real-World Impact- (Semester 7-8)
Undertake a significant final year project that addresses a real-world problem using advanced AI/ML techniques. Focus on innovation, scalability, and impact. Document the project thoroughly and prepare for a strong defense.
Tools & Resources
Project management tools, cloud platforms (AWS, GCP, Azure), open-source AI frameworks, research papers
Career Connection
A strong capstone project is a key differentiator, showcasing practical problem-solving skills and the ability to deliver complex AI solutions, crucial for job interviews and research roles.
Specialize and Build a Personal Brand- (Semester 6-8)
Choose electives and project topics that align with a specific AI specialization (e.g., NLP, Computer Vision, Robotics, Reinforcement Learning). Create a professional online presence through GitHub, LinkedIn, and potentially a personal blog or website.
Tools & Resources
GitHub, LinkedIn, Medium/Dev.to for blogging, personal website builders
Career Connection
Specialization and a strong personal brand help in targeting specific high-demand roles and showcasing expertise to potential employers, leading to better career opportunities.
Master Interview and Professional Communication Skills- (Semester 6-8)
Practice technical interviews, aptitude tests, and soft skills required for job placements. Engage in mock interviews, refine resume and cover letter writing, and develop strong presentation and negotiation abilities.
Tools & Resources
InterviewBit, LeetCode, HackerRank for interview prep, mock interview platforms, career counseling services
Career Connection
Excellent interview skills, combined with a strong technical background, are paramount for securing desirable placements and navigating career progression effectively in the Indian tech industry.
Program Structure and Curriculum
Eligibility:
- Minimum 60% aggregate in 10+2 with Physics, Chemistry, and Mathematics (PCM) or Physics, Chemistry, Mathematics, and Biology (PCMB). Selection based on Amity JEE/JEE Mains/UniGauge-E score followed by an interview (as per official university website).
Duration: 4 years (8 semesters)
Credits: 178 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTH101 | Engineering Mathematics - I | Core | 3 | Differential Calculus, Integral Calculus, Matrices, Vector Algebra, Sequences & Series |
| PHY101 | Engineering Physics | Core | 3 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers & Optical Fibers, Nanomaterials |
| CHY101 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion & its control, Fuels & Lubricants, Polymers |
| EEE101 | Basic Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Diodes & Transistors, Digital Electronics Basics |
| CS101 | Computer Programming - I | Core | 3 | Introduction to Programming, Data Types & Variables, Control Structures, Functions, Arrays |
| EVS101 | Environmental Studies | Core | 2 | Ecosystems, Biodiversity, Environmental Pollution, Social Issues & Environment, Human Population & Environment |
| HUL101 | Communication Skills - I | Core | 2 | Basic English Grammar, Reading Comprehension, Public Speaking, Presentation Skills, Group Discussion |
| ME101 | Engineering Graphics | Lab | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Drafting with CAD Software, Dimensioning |
| PHY102 | Engineering Physics Lab | Lab | 1 | Experiments on Optics, Electricity, Mechanics, Material Properties |
| CHY102 | Engineering Chemistry Lab | Lab | 1 | Water quality analysis, pH determination, Acid-base titrations, Polymer synthesis |
| EEE102 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Verification of Circuit Laws, Characteristics of Diodes/Transistors, Digital Logic Gates |
| CS102 | Computer Programming - I Lab | Lab | 1 | C/C++ Programming Practice, Problem Solving, Debugging Techniques, Implementing Basic Algorithms |
| VAC101 | Value Added Course | Value Added | 1 | Introduction to Values, Ethics, Social Responsibility |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTH201 | Engineering Mathematics - II | Core | 3 | Linear Algebra, Differential Equations, Laplace Transforms, Fourier Series, Complex Analysis |
| CS201 | Data Structures | Core | 3 | Arrays & Linked Lists, Stacks & Queues, Trees & Binary Trees, Graphs & Graph Traversal, Hashing Techniques |
| CS203 | Object-Oriented Programming (OOP) with C++ | Core | 3 | Classes & Objects, Inheritance, Polymorphism, Abstraction & Encapsulation, Exception Handling |
| ECE201 | Digital Electronics | Core | 3 | Boolean Algebra & Logic Gates, Combinational Circuits, Sequential Circuits, Counters & Registers, Memory Devices |
| MTH203 | Discrete Mathematics | Core | 3 | Set Theory, Logic & Proofs, Relations & Functions, Graph Theory, Recurrence Relations |
| CS205 | Computer Architecture & Organization | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Pipelining, Instruction Sets |
| HUL201 | Professional Communication | Core | 2 | Business Correspondence, Technical Report Writing, Interview Skills, Group Discussions, Presentation Techniques |
| CS202 | Data Structures Lab | Lab | 1 | Implementation of data structures, Algorithm analysis, Problem-solving using DSA |
| CS204 | Object-Oriented Programming (OOP) with C++ Lab | Lab | 1 | C++ programming exercises, Object-oriented design patterns, Project-based OOP implementation |
| ECE202 | Digital Electronics Lab | Lab | 1 | Logic gate implementation, Combinational and Sequential circuits, Digital system design |
| VAC201 | Value Added Course | Value Added | 1 | Personal Development, Interpersonal Skills, Career Planning Basics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| CS303 | Database Management Systems | Core | 3 | Relational Model & SQL, ER Diagrams & Normalization, Transaction Management, Concurrency Control, Database Security |
| CS305 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity, Sorting & Searching, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| CS307 | Artificial Intelligence (Foundation) | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Agents |
| MTH301 | Probability and Statistics | Core | 3 | Probability Theory, Random Variables & Distributions, Hypothesis Testing, Regression Analysis, Correlation |
| CS309 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Testing & Quality Assurance, Project Management |
| CS302 | Operating Systems Lab | Lab | 1 | Shell scripting, Process management in Linux, Memory management concepts |
| CS304 | Database Management Systems Lab | Lab | 1 | SQL queries & database design, PL/SQL programming, Database administration tasks |
| CS308 | Artificial Intelligence Lab | Lab | 1 | Implementing search algorithms, Logic programming (Prolog/Python), AI problem-solving frameworks |
| HUL301 | Soft Skills | Core | 1 | Communication Skills, Teamwork, Leadership, Conflict Resolution, Time Management |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Decidability |
| CS403 | Computer Networks | Core | 3 | OSI/TCP-IP Model, Networking Devices, Addressing & Routing Protocols, Transport Layer, Application Layer Protocols |
| CS405 | Web Technologies | Core | 3 | HTML, CSS, JavaScript, Web Servers & Client-Server Architecture, AJAX & DOM, PHP/Python for Web Development, Web Security Basics |
| CS407 | Machine Learning (Foundation) | Core | 3 | Introduction to ML, Supervised & Unsupervised Learning, Regression & Classification, Model Evaluation Metrics, Bias-Variance Tradeoff |
| CS409 | Data Science Fundamentals | Core | 3 | Data Collection & Cleaning, Data Visualization, Exploratory Data Analysis, Feature Engineering, Introduction to Data Mining |
| CS411 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| CS404 | Computer Networks Lab | Lab | 1 | Socket Programming, Network configuration, Protocol analysis (Wireshark), Network simulation |
| CS406 | Web Technologies Lab | Lab | 1 | Developing dynamic web pages, Database integration with web, Frontend framework basics |
| CS408 | Machine Learning Lab | Lab | 1 | Implementing ML algorithms in Python, Scikit-learn usage, Model training and evaluation |
| PJ401 | Minor Project - I | Project | 2 | Project Planning, Implementation, Documentation, Presentation, Basic Software Development Life Cycle |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases, Data Streaming |
| CS503 | Deep Learning | Core | 3 | Neural Networks & Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs & GRUs, Autoencoders & GANs |
| CS505 | Natural Language Processing | Core | 3 | NLP Basics & Text Preprocessing, Word Embeddings, POS Tagging & NER, Sentiment Analysis, Machine Translation |
| CS507 | Computer Vision | Core | 3 | Image Processing Basics, Feature Detection & Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| EC501 | Elective - I | Elective | 3 | Advanced topics in chosen specialization, Emerging technologies, Industry-specific applications |
| CS502 | Big Data Analytics Lab | Lab | 1 | Hands-on with Hadoop, Spark programming, NoSQL database queries |
| CS504 | Deep Learning Lab | Lab | 1 | Implementing deep learning models, TensorFlow/PyTorch practice, Training neural networks |
| CS506 | Natural Language Processing Lab | Lab | 1 | Implementing NLP tasks (NLTK/SpaCy), Text analysis applications, Sentiment analysis projects |
| CS508 | Computer Vision Lab | Lab | 1 | Image processing with OpenCV, Object detection & recognition, Building vision applications |
| PJ501 | Minor Project - II | Project | 2 | Advanced project development, AI/ML domain specific projects, Problem-solving & implementation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Value & Policy Iteration, Q-Learning & SARSA, Deep Reinforcement Learning, Exploration-Exploitation Tradeoff |
| CS603 | AI Ethics and Governance | Core | 3 | Ethical AI Principles, Bias in AI & Fairness, AI Regulation & Policy, Data Privacy & Security, Transparency & Explainability in AI |
| CS605 | Cloud Computing for AI | Core | 3 | Cloud Architectures, AWS/Azure/GCP for AI, Serverless Computing, MLOps & Deployment, Scalable AI Solutions |
| EC601 | Elective - II | Elective | 3 | Specialized AI/ML concepts, Research areas in AI, Advanced programming paradigms |
| EC603 | Elective - III | Elective | 3 | Cross-disciplinary applications of AI, Emerging AI technologies, Data governance and compliance |
| CS602 | Reinforcement Learning Lab | Lab | 1 | Implementing RL algorithms (OpenAI Gym), Agent training scenarios, Performance evaluation of RL agents |
| CS606 | Cloud Computing for AI Lab | Lab | 1 | Deploying AI models on cloud platforms, Cloud services for ML (Sagemaker, Azure ML), Containerization (Docker) |
| IN601 | Industry Internship/Training | Internship | 6 | Real-world project experience, Industry practices, Professional skill development, Report writing & Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS701 | Robotics and Intelligent Systems | Core | 3 | Robot Kinematics & Dynamics, Path Planning Algorithms, Sensor Fusion, AI in Robotics, Human-Robot Interaction |
| CS703 | Human-Computer Interaction (HCI) | Core | 3 | User Interface Design, Usability Principles, User Experience (UX), Interaction Models, AI in HCI |
| EC701 | Elective - IV | Elective | 3 | Cognitive computing, Bio-inspired AI, Quantum computing basics |
| EC703 | Elective - V | Elective | 3 | AI for cybersecurity, AI in healthcare, Financial AI applications |
| PJ701 | Project - I | Project | 6 | Research & Design Phase, Development of a major AI project, Technical Documentation, Project Presentation, Problem-solving methodology |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS801 | Advanced AI Topics (Seminar/Research) | Core | 3 | Current Trends in AI, Research Methodologies, Advanced Machine Learning, Presentation Skills, Critical Analysis of AI Papers |
| EC801 | Elective - VI | Elective | 3 | Advanced Data Mining, Edge AI, Generative Models |
| EC803 | Elective - VII | Elective | 3 | IoT and AI integration, Explainable AI (XAI), Federated Learning |
| PJ801 | Project - II (Major Project) | Project | 12 | Comprehensive AI system development, Thesis Writing, Project Defense, Innovation & Impact Assessment, Scalability & Optimization |
