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B-TECH in Artificial Intelligence Machine Learning at Sharda University
Gautam Buddh Nagar, Uttar Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Sharda University Gautam Buddh Nagar?
This B.Tech Artificial Intelligence & Machine Learning program at Sharda University focuses on cutting-edge AI and ML principles and applications. It equips students with skills for India''''s rapidly expanding tech industry, where demand for AI/ML professionals is surging across healthcare, finance, and e-commerce, driving innovation.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and problem-solving, seeking high-growth tech roles. It also suits working professionals aiming to upskill in AI/ML or career changers transitioning to this transformative domain.
Why Choose This Course?
Graduates can expect diverse career paths in India as AI Engineers, Machine Learning Scientists, or Data Scientists. Entry-level salaries range from INR 4-8 LPA with significant growth. The program prepares students for professional certifications and roles in top Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Develop strong problem-solving skills and master programming in C/C++/Python. Focus on data structures and algorithms, which are foundational for AI/ML. Actively solve problems on coding platforms to build proficiency.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Essential for cracking technical interviews and building efficient AI/ML models in future roles.
Cultivate Academic Excellence- (Semester 1-2)
Prioritize understanding core engineering subjects like Mathematics and Physics. Form study groups, attend tutorials, and clarify doubts immediately. Aim for a strong CGPA, crucial for internships and higher studies.
Tools & Resources
University library resources, NPTEL, Khan Academy, Peer study groups
Career Connection
High academic scores open doors to better internships and placement opportunities in competitive Indian companies.
Explore AI/ML Basics- (Semester 1-2)
Even in early semesters, start exploring basic concepts of AI/ML through online courses or workshops. Understand the scope and applications to build early interest and guide future learning and project choices.
Tools & Resources
Coursera (Andrew Ng''''s ML course), edX, YouTube tutorials, Local university workshops
Career Connection
Early exposure helps in choosing relevant projects and internships later, clarifying long-term career goals.
Intermediate Stage
Practical Project Development- (Semester 3-5)
Apply theoretical knowledge by undertaking mini-projects in AI/ML, focusing on building real-world applications using Python libraries. Actively participate in hackathons and coding competitions to enhance skills.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn, TensorFlow/PyTorch
Career Connection
Projects build a strong portfolio, demonstrating practical skills and problem-solving abilities to recruiters.
Seek Industry Exposure & Internships- (Semester 4-5)
Actively search for summer internships in AI/ML roles within startups or established companies across India. Network with professionals, attend industry events, and leverage university career services.
Tools & Resources
LinkedIn, Internshala, University placement cell, Industry meetups and workshops
Career Connection
Internships provide invaluable real-world experience, often leading to pre-placement offers and professional networking.
Specialize in Key AI/ML Areas- (Semester 4-5)
Based on interest, delve deeper into specific areas like Deep Learning, NLP, or Computer Vision. Take advanced online courses, read research papers, and work on specialized projects to build expertise.
Tools & Resources
ArXiv, Specific deep learning frameworks documentation, Specialized online courses from edX/Coursera
Career Connection
Specialization makes you a more attractive candidate for niche roles in the competitive Indian AI/ML industry.
Advanced Stage
Undertake Capstone Projects & Research- (Semester 7-8)
Focus on a substantial final year project (Major Project-I & II) that solves a real-world AI/ML problem. Consider collaborating with faculty on research papers or participating in academic conferences to showcase work.
Tools & Resources
University labs, Research journals, Faculty guidance, High-performance computing resources
Career Connection
Demonstrates advanced problem-solving, research aptitude, and innovation, enhancing opportunities for jobs or higher studies.
Intensive Placement Preparation- (Semester 7-8)
Begin intensive preparation for placements, including mock interviews, aptitude tests, technical rounds, and HR interviews. Refine your resume and LinkedIn profile. Practice soft skills and group discussions.
Tools & Resources
Placement cell workshops, Online aptitude test platforms, Interview prep platforms like LeetCode, Mock interview services
Career Connection
Crucial for securing desirable job offers from top companies visiting campus or through off-campus drives.
Network and Professional Development- (Semester 6-8)
Expand your professional network by connecting with alumni, industry leaders, and mentors. Attend workshops, seminars, and guest lectures. Consider pursuing relevant professional certifications in AI/ML.
Tools & Resources
LinkedIn, Professional organizations (e.g., IEEE), Industry webinars, Certification platforms like AWS ML, Google Cloud AI
Career Connection
Opens doors to hidden job markets, mentorship, and continuous learning opportunities throughout your career in AI/ML.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with PCM/PCB/PCMB with minimum 50% marks and with minimum 50% marks in Mathematics. Appeared in JEE Main/ SUAT (admission based on score in JEE Main/SUAT; if not taken, admission based on 10+2 marks).
Duration: 4 years (8 semesters)
Credits: 169 Credits
Assessment: Internal: 30% (Theory), 50% (Practical), External: 70% (Theory), 50% (Practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAS101 | Engineering Mathematics-I | Core | 4 | Matrices, Differential Calculus-I, Differential Calculus-II, Partial Differentiation & its Applications, Multiple Integrals |
| BAS1001 | Engineering Physics | Core | 4 | Relativity, Quantum Mechanics, Wave Optics, Optical Fiber & Laser, Solid State Physics |
| BCE101 | Basic Electrical & Electronics Engineering | Core | 4 | DC & AC Circuits, Transformer & DC Machines, Semiconductor Devices, Amplifiers & Oscillators, Digital Electronics |
| BEE101 | Introduction to Computer Science & Engineering | Core | 3 | Introduction to Computer, Problem Solving using C, Control Statements, Functions, Arrays, Pointers, Structures, Unions, Files |
| BAS1002 | Engineering Physics Lab | Lab | 1 | Wavelength measurement, Specific rotation, Plank''''s constant, Numerical aperture, Hall effect |
| BCE102 | Basic Electrical & Electronics Engineering Lab | Lab | 1 | Basic circuit components, Ohm''''s law, PN junction diode, Transistor characteristics, Logic gates |
| BEE102 | Introduction to Computer Science & Engineering Lab | Lab | 1 | Basic C programs, Control structures, Functions, Arrays, Pointers, Structures, Files operations |
| BCS1001 | Computer Workshop | Lab | 1 | Basics of hardware, Software installation, Network configuration, PC assembly, OS commands |
| BGS101 | Professional Communication Skills | Core | 2 | Oral Communication, Listening Comprehension, Reading Skills, Writing Skills, Presentation Skills |
| BGS102 | Professional Communication Skills Lab | Lab | 1 | Group Discussions, Mock Interviews, Presentations, Role plays, Debates |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAS201 | Engineering Mathematics-II | Core | 4 | Differential Equations, Laplace Transforms, Fourier Series, Vector Calculus-I, Vector Calculus-II |
| BAS2001 | Engineering Chemistry | Core | 4 | Water Technology, Fuels & Combustion, Polymers & Composites, Electrochemistry & Corrosion, Engineering Materials |
| BME201 | Engineering Graphics & Design | Core | 3 | Introduction to Engineering Graphics, Orthographic Projections, Sectional Views, Isometric Projections, Computer Aided Drafting |
| BCS201 | Data Structures | Core | 3 | Introduction to Data Structures, Arrays, Linked Lists, Stacks & Queues, Trees, Graphs, Sorting & Searching |
| BCS202 | Object Oriented Programming | Core | 3 | Introduction to OOP, Classes & Objects, Inheritance, Polymorphism, Abstraction, Encapsulation, Exception Handling, File I/O |
| BAS2002 | Engineering Chemistry Lab | Lab | 1 | Water hardness determination, Viscosity measurement, Acid-base titrations, Electrochemical cells experiments, Polymer synthesis |
| BME202 | Engineering Graphics & Design Lab | Lab | 1 | Orthographic projections practice, Sectional views drawing, Isometric views creation, AutoCAD practice sessions, Solid modeling exercises |
| BCS203 | Data Structures Lab | Lab | 1 | Array implementations, Linked list operations, Stack/Queue applications, Tree traversals, Graph algorithms |
| BCS204 | Object Oriented Programming Lab | Lab | 1 | Class/object implementation, Inheritance examples, Polymorphism concepts, Exception handling programs, File I/O programming |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS301 | Discrete Mathematics | Core | 4 | Set Theory, Relations & Functions, Propositional Logic, Predicate Logic, Combinatorics, Graph Theory, Algebraic Structures |
| BCS302 | Database Management System | Core | 3 | Introduction to DBMS, ER Model, Relational Model, SQL, Relational Algebra, Normalization, Transaction Management, Concurrency Control |
| BCS303 | Operating System | Core | 3 | Introduction to OS, Process Management, CPU Scheduling, Deadlocks, Memory Management, Virtual Memory, File Systems, I/O Systems |
| BEE301 | Digital Logic & Design | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Registers & Counters, Memory Units, PLDs |
| BAI301 | Introduction to Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving, Search Algorithms, Heuristic Search, Knowledge Representation, Logic Programming, Expert Systems |
| BAI302 | Introduction to Machine Learning | Core | 3 | Introduction to ML, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Classification, Model Evaluation, Overfitting |
| BCS304 | Database Management System Lab | Lab | 1 | SQL queries practice, DDL/DML commands, Join operations, Stored procedures, Trigger implementation |
| BCS305 | Operating System Lab | Lab | 1 | Linux commands, Shell scripting, Process creation & management, CPU scheduling algorithms, Memory management simulations |
| BEE302 | Digital Logic & Design Lab | Lab | 1 | Logic gate verification, Adder/Subtractor design, Flip-flops implementation, Counters construction, Shift registers circuits |
| BAI303 | Artificial Intelligence Lab | Lab | 1 | Python basics for AI, Search algorithm implementation, Knowledge representation (Prolog/Python), Mini AI project development, Problem-solving agents |
| BAI304 | Machine Learning Lab | Lab | 1 | Python for ML, Libraries (Scikit-learn), Data preprocessing techniques, Regression model building, Classification algorithm implementation, Model evaluation metrics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAS401 | Engineering Mathematics-III | Core | 4 | Probability & Statistics, Complex Analysis, Numerical Methods, Transform Techniques, Statistical Inference |
| BCS401 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis, Asymptotic Notations, Divide & Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness, Randomized Algorithms |
| BCS402 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability, Chomsky Hierarchy |
| BAI401 | Advanced Machine Learning | Core | 3 | Ensemble Methods, Support Vector Machines, Neural Networks, Deep Learning Fundamentals, Dimensionality Reduction, Clustering Algorithms, Recommender Systems |
| BAI402 | Natural Language Processing | Core | 3 | NLP Basics, Text Preprocessing, N-grams, Part-of-Speech Tagging, Sentiment Analysis, Text Classification, Word Embeddings, Machine Translation |
| BCE401 | Environmental Studies | Core | 2 | Ecosystems, Biodiversity, Environmental Pollution, Natural Resources, Sustainable Development, Environmental Ethics |
| BCS403 | Design & Analysis of Algorithms Lab | Lab | 1 | Implementation of sorting algorithms, Searching algorithms, Dynamic programming problems, Graph traversal algorithms, Complexity analysis |
| BAI403 | Advanced Machine Learning Lab | Lab | 1 | Ensemble methods implementation, SVM applications, Introduction to deep learning frameworks, Advanced clustering techniques, Cross-validation methods |
| BAI404 | Natural Language Processing Lab | Lab | 1 | Text cleaning, Tokenization, POS tagging using NLTK, Sentiment analysis implementation, Word embedding generation, Basic chatbot development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS501 | Computer Networks | Core | 3 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer, Network Security Basics |
| BCS502 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirement Engineering, Design Principles & Patterns, Software Testing & Validation, Project Management, Software Quality |
| BAI501 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Transformers, Generative Models (GANs, VAEs), Deep RL |
| BAI502 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition, Scene Understanding, 3D Vision |
| BEC501 | Big Data Analytics | Elective (Department Elective-I) | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, Data Warehousing Concepts, Data Mining Techniques |
| OECXXX | Open Elective-I | Elective (Open Elective) | 3 | |
| BCS503 | Computer Networks Lab | Lab | 1 | Network commands, Socket programming, Protocol simulation, Network configuration, Wireshark analysis |
| BAI503 | Deep Learning Lab | Lab | 1 | Implement CNNs, RNNs, LSTMs, Image classification tasks, Text generation using deep models, TensorFlow/Keras/PyTorch practice, Hyperparameter tuning |
| BAI504 | Computer Vision Lab | Lab | 1 | Image manipulation (OpenCV), Edge detection algorithms, Object recognition systems, Face detection techniques, Image segmentation methods |
| BGS501 | Professional Ethics & Values | Core | 2 | Ethical Theories, Professionalism in Engineering, Cyber Ethics & Privacy, Environmental Ethics, Corporate Social Responsibility |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAI601 | Reinforcement Learning | Core | 3 | Introduction to RL, MDPs, Dynamic Programming, Monte Carlo Methods, TD Learning, Q-Learning, Policy Gradient Methods, Deep Reinforcement Learning |
| BAI602 | AI for Robotics | Core | 3 | Robotics Fundamentals, Robot Kinematics & Dynamics, Motion Planning & Control, Robot Vision, Sensor Fusion, AI in Autonomous Systems, HRI |
| BCS601 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Target Code Generation |
| BEC601 | IoT & its Applications | Elective (Department Elective-II) | 3 | Introduction to IoT, IoT Architecture, Sensors & Actuators, Communication Protocols (MQTT, CoAP), IoT Platforms & Data Analytics, Security & Privacy in IoT |
| BCS602 | Distributed Systems | Elective (Department Elective-III) | 3 | Introduction to Distributed Systems, Client/Server Model, RPC, Distributed File Systems, Concurrency Control, Fault Tolerance, Replication |
| OECXXX | Open Elective-II | Elective (Open Elective) | 3 | |
| BAI603 | Reinforcement Learning Lab | Lab | 1 | Implement Q-learning, SARSA, Policy gradient methods, OpenAI Gym environments, Basic robot control simulations, Exploration-exploitation strategies |
| BAI604 | AI for Robotics Lab | Lab | 1 | Robot simulation software (ROS, Gazebo), Kinematics programming, Path planning algorithms, Sensor data processing, Control system implementation |
| BCS603 | Minor Project | Project | 2 | Project Planning & Scope Definition, Requirement Analysis, Design & Implementation, Testing & Debugging, Documentation & Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS701 | Ethical Hacking & Cyber Security | Elective (Department Elective-IV) | 3 | Introduction to Cyber Security, Network Security, Web Application Security, Malware Analysis, Cryptography & Steganography, Ethical Hacking Techniques |
| BEC701 | Cloud Computing | Elective (Department Elective-V) | 3 | Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technologies, Cloud Security, Cloud Management |
| OECXXX | Open Elective-III | Elective (Open Elective) | 3 | |
| BAI701 | Major Project-I | Project | 4 | Advanced project planning, Literature review & problem definition, Prototype development, Module testing & integration, Interim report writing |
| BAI702 | Industrial Training/Internship | Internship | 3 | Industry exposure, Practical skill development, Professional networking, Report writing & presentation, Real-world problem solving |
| BGS701 | Universal Human Values & Ethics | Core | 3 | Understanding Harmony, Family & Society, Nature & Existence, Holistic Perception, Professional Ethics & Conduct |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAI801 | Pattern Recognition | Elective (Department Elective-VI) | 3 | Introduction to Pattern Recognition, Statistical Pattern Recognition, Dimensionality Reduction, Clustering, Classification Techniques, Feature Selection, Neural Networks for PR |
| BAI802 | Data Visualization | Elective (Department Elective-VII) | 3 | Principles of Data Visualization, Data Types & Visual Encodings, Chart Types & Dashboards, Interactive Visualization, Visualization Tools (Tableau, D3.js) |
| OECXXX | Open Elective-IV | Elective (Open Elective) | 3 | |
| BAI803 | Major Project-II | Project | 6 | Final project implementation, Comprehensive testing & validation, Performance evaluation, Thesis writing & documentation, Final presentation & defense |
| BAI804 | Seminar | Project | 2 | Research topic selection, Literature survey, Advanced presentation skills, Technical report writing, Q&A handling & discussion |
