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B-TECH in Artificial Intelligence at Shoolini University of Biotechnology and Management Sciences
Solan, Himachal Pradesh
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About the Specialization
What is Artificial Intelligence at Shoolini University of Biotechnology and Management Sciences Solan?
This B.Tech Computer Science & Engineering with Specialization in Artificial Intelligence program at Shoolini University focuses on equipping students with advanced knowledge and practical skills in AI. It addresses the growing demand for AI professionals in the Indian industry, preparing graduates for roles in machine learning, deep learning, NLP, and robotics. The curriculum is designed to be industry-aligned, incorporating collaborative inputs from leading tech companies, making it highly relevant to current market needs.
Who Should Apply?
This program is ideal for ambitious fresh graduates holding a 10+2 qualification with strong Physics, Maths, and Computer Science/Biology backgrounds, who aspire to build a career in the rapidly evolving AI sector. It also caters to individuals with a foundational understanding of computer science seeking to specialize in AI. The program is suitable for those passionate about problem-solving through intelligent systems and keen to contribute to India''''s digital transformation.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as AI engineers, Machine Learning specialists, Data Scientists, Robotics engineers, and AI consultants in tech giants, startups, and research institutions across India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The strong industry alignment aids in securing placements and provides a solid foundation for further studies or entrepreneurial ventures in the Indian tech landscape.
Student Success Practices
Foundation Stage
Build a Strong Programming Foundation- (Semester 1-2)
Consistently practice programming concepts learned in C/C++ and Python. Focus on understanding data types, control flow, functions, and object-oriented principles. Participate in online coding challenges on platforms like HackerRank or LeetCode to enhance problem-solving skills and algorithmic thinking.
Tools & Resources
C/C++ Compilers (GCC, Visual Studio), Python IDLE/Jupyter Notebook, HackerRank, LeetCode, GeeksforGeeks
Career Connection
Solid programming skills are fundamental for all tech roles, especially in AI. They are critical for clearing technical interviews and efficiently implementing AI algorithms later.
Master Mathematical & Statistical Fundamentals- (Semester 1-2)
Dedicate significant time to understanding calculus, linear algebra, probability, and statistics. These are the mathematical pillars of AI and machine learning. Utilize resources like Khan Academy, NPTEL courses, and practice problems to solidify your grasp on these concepts.
Tools & Resources
Khan Academy, NPTEL, Online textbooks, Wolfram Alpha, MATLAB/SciPy for mathematical computation
Career Connection
A strong mathematical background is essential for comprehending, developing, and optimizing complex AI models, directly impacting roles in R&D and advanced AI engineering.
Develop Effective Communication & Soft Skills- (Semester 1-2)
Actively participate in communication skills labs, group discussions, and presentations. Focus on improving English proficiency, public speaking, and technical writing. Join student clubs or debate societies to build confidence and interpersonal skills crucial for teamwork.
Tools & Resources
Toastmasters International (if available), Grammarly, Presentation software (PowerPoint, Google Slides), University communication workshops
Career Connection
Excellent communication skills are vital for conveying technical ideas to diverse audiences, collaborating in teams, and excelling in interviews and client interactions.
Intermediate Stage
Dive Deep into Data Structures & Algorithms- (Semester 3-5)
Beyond theoretical understanding, implement various data structures (trees, graphs, hash tables) and algorithms (sorting, searching, dynamic programming) in C++ and Python. Focus on optimizing time and space complexity. Prepare for competitive programming contests.
Tools & Resources
CodeForces, TopCoder, InterviewBit, GeeksforGeeks, Visual Studio Code, Jupyter Notebook
Career Connection
Strong DSA skills are a primary filter for almost all software development and AI/ML engineering roles in product-based companies, demonstrating analytical and problem-solving abilities.
Engage in AI/ML Project-Based Learning- (Semester 4-5)
Start working on small AI/ML projects, even toy problems, using Python libraries like Scikit-learn, NumPy, and Pandas. Utilize publicly available datasets on platforms like Kaggle. Focus on applying learned concepts of Machine Learning, starting from simple classification to more complex models.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, TensorFlow/Keras (for initial exposure), Scikit-learn
Career Connection
Practical project experience is crucial for building a portfolio, understanding real-world challenges, and showcasing applied skills to potential employers in the AI domain.
Seek Early Industry Exposure & Networking- (Semester 4-5)
Actively pursue internships during semester breaks (e.g., 4-week internship in Sem 4). Attend industry webinars, workshops, and career fairs organized by the university. Connect with alumni and industry professionals on LinkedIn to gain insights and explore opportunities.
Tools & Resources
LinkedIn, University Career Services, Industry webinars/conferences, Internship platforms (Internshala, LetsIntern)
Career Connection
Internships provide invaluable hands-on experience and a glimpse into corporate culture. Networking opens doors to mentorship, job leads, and understanding industry trends, crucial for placements.
Advanced Stage
Specialise and Build an Advanced AI Portfolio- (Semester 6-8)
Focus on deepening knowledge in specific AI domains like Deep Learning, NLP, Computer Vision, or Robotics based on your interests and elective choices. Undertake significant projects (Mini Project-I, Project-I, Project-II) that showcase complex AI applications and innovative solutions. Publish research papers if applicable.
Tools & Resources
TensorFlow, PyTorch, OpenCV, NLTK, Hugging Face, ROS, Git/GitHub for version control
Career Connection
A specialized and impactful project portfolio is the cornerstone for securing advanced roles in AI research, development, and engineering, demonstrating expertise and initiative.
Prepare Rigorously for Placements & Higher Studies- (Semester 7-8)
Begin extensive preparation for technical interviews, focusing on AI/ML concepts, DSA, and system design. Practice mock interviews. If aiming for higher studies, prepare for GRE/GATE and research potential universities/research groups. Leverage the university''''s placement cell resources and attend resume-building workshops.
Tools & Resources
LeetCode Premium, Glassdoor, Interview experiences, GRE/GATE preparation materials, University Placement Cell
Career Connection
Comprehensive preparation directly translates into successful placements at top companies or admission to prestigious graduate programs, leading to accelerated career growth.
Engage in Research and Industry Collaborations- (Semester 6-8)
Actively participate in the industrial training program (6 weeks in Sem 6). Explore opportunities to work with faculty on research projects or collaborate with industry partners on real-world challenges. Contribute to open-source AI projects.
Tools & Resources
Research journals, GitHub, Industry research labs, University-industry collaboration cells
Career Connection
Research experience and industry collaboration enhance problem-solving skills, provide exposure to cutting-edge technologies, and differentiate candidates for specialized roles and R&D positions.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Maths, Chemistry/Biotech/Computer Science/Biology with 60% aggregate. Valid JEE/SAT/HPCET score is desirable.
Duration: 8 semesters / 4 years
Credits: 170 Credits
Assessment: Internal: 40% (for theory subjects), 60% (for practical/project subjects), External: 60% (for theory subjects), 40% (for practical/project subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCS-101 | Applied Physics | Core | 4 | Wave Optics, Lasers and Fibre Optics, Quantum Mechanics, Solid State Physics, Semiconductor Devices |
| BTAM-101 | Applied Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Sequences and Series |
| BTCS-103 | Introduction to Computer Science & Engineering | Core | 3 | Computer Fundamentals, Introduction to Programming, Operating Systems Basics, Networking Concepts, Software Development Life Cycle |
| BTHU-101 | Communication Skills | Core | 3 | Basic Grammar, Reading Comprehension, Public Speaking, Presentation Skills, Report Writing |
| BTCS-105 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Basic Electronics |
| BTCS-107 | Environmental Sciences | Core | 3 | Ecosystems, Biodiversity, Pollution and Control, Climate Change, Sustainable Development |
| BTCS-151 | Applied Physics Lab | Lab | 1 | Optical Experiments, Laser Characteristics, Fiber Optics Communication, Semiconductor Device Testing, Measurement and Error Analysis |
| BTCS-153 | Computer Programming Lab | Lab | 1 | C Language Fundamentals, Conditional Statements and Loops, Functions and Arrays, Pointers, Basic Algorithm Implementation |
| BTHU-151 | Communication Skills Lab | Lab | 1 | Group Discussions, Interview Skills, Public Speaking Practice, Presentation Practice, Role Plays and Debates |
| BTCS-155 | Basic Electrical Engineering Lab | Lab | 1 | Circuit Laws Verification, AC Circuit Analysis, Transformer Experiment, Diode and Transistor Characteristics, Motor Principles |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCH-101 | Engineering Chemistry | Core | 4 | Water Technology, Polymer Chemistry, Corrosion and its Control, Fuels and Lubricants, Spectroscopic Techniques |
| BTAM-103 | Applied Mathematics-II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Numbers |
| BTCS-102 | Programming for Problem Solving | Core | 3 | C++ Basics, Object-Oriented Programming Concepts, Arrays and Strings, Pointers and Dynamic Memory, File Handling |
| BTCS-104 | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Computer Aided Design (CAD), Assembly Drawings |
| BTME-101 | Manufacturing Processes | Core | 3 | Casting Processes, Forming Processes, Machining Processes, Welding Processes, Joining Processes |
| BTCH-151 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Viscosity Determination, Polymer Synthesis, pH Metry, Colorimetry |
| BTCS-152 | Programming for Problem Solving Lab | Lab | 1 | C++ Programming Exercises, OOP Implementation, Debugging Techniques, Array and String Manipulation, File I/O Operations |
| BTCS-154 | Engineering Graphics & Design Lab | Lab | 1 | 2D Drafting, 3D Modeling, Assembly Modeling, Sectional Drawing, CAD Software Proficiency |
| BTME-151 | Manufacturing Practices Workshop | Lab | 1 | Carpentry Shop, Fitting Shop, Welding Shop, Foundry Shop, Machine Shop |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAM-201 | Applied Mathematics-III | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Statistical Inference, Regression and Correlation |
| BTCS-201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs, Sorting and Searching Algorithms |
| BTCS-203 | Object Oriented Programming | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstraction and Encapsulation, Exception Handling |
| BTCS-205 | Digital Electronics & Logic Design | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory Elements |
| BTCS-207 | Computer Organization & Architecture | Core | 3 | Computer Components, CPU Design, Memory Hierarchy, I/O Organization, Instruction Sets |
| BTHU-201 | Universal Human Values | Core | 3 | Self-Exploration, Harmony in Family and Society, Understanding Nature, Professional Ethics, Holistic Development |
| BTCS-251 | Data Structures Lab | Lab | 1 | Array Operations, Linked List Implementations, Stack and Queue Applications, Tree Traversal Algorithms, Graph Algorithms |
| BTCS-253 | Object Oriented Programming Lab | Lab | 1 | C++ OOP Programs, Inheritance and Polymorphism Examples, Templates, Exception Handling Implementation, File I/O with Objects |
| BTCS-255 | Digital Electronics Lab | Lab | 1 | Logic Gate Verification, Combinational Circuit Design, Flip-Flops, Counters and Registers, Multiplexers and Demultiplexers |
| BTCS-257 | Computer Organization & Architecture Lab | Lab | 1 | Assembly Language Programming, CPU Simulation, Memory Interfacing, I/O Operations, Performance Analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCS-202 | Discrete Mathematics | Core | 4 | Set Theory, Mathematical Logic, Relations and Functions, Graph Theory, Algebraic Structures |
| BTCS-204 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks and Concurrency |
| BTCS-206 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis, Sorting Algorithms, Searching Algorithms, Greedy Algorithms, Dynamic Programming |
| BTCS-208 | Database Management System | Core | 3 | ER Model, Relational Model, SQL Queries, Normalization, Transaction and Concurrency Control |
| BTCS-210 | Python Programming | Core | 3 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Python, File I/O and Exception Handling |
| BTCS-252 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management Commands, Thread Synchronization, Memory Allocation Algorithms, Deadlock Detection and Prevention |
| BTCS-254 | Design & Analysis of Algorithms Lab | Lab | 1 | Sorting Algorithm Implementations, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithms, Time Complexity Analysis |
| BTCS-256 | Database Management System Lab | Lab | 1 | SQL Queries and Joins, Database Schema Design, PL/SQL Programming, Database Connectivity (JDBC/ODBC), Transaction Management |
| BTCS-258 | Python Programming Lab | Lab | 1 | Python Scripting, Data Manipulation with Pandas, Data Visualization with Matplotlib, Web Scraping, Introduction to Machine Learning Libraries |
| BTCS-260 | Industry Internship (4 Weeks) | Project | 2 | Industrial Work Exposure, Project Report Writing, Presentation Skills, Problem Solving in Industry, Professional Communication |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCS-301 | Formal Languages & Automata Theory | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| BTCS-303 | Computer Networks | Core | 3 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer, Transport and Application Layers |
| BTCS-305 | Software Engineering | Core | 3 | Software Development Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing, Software Project Management |
| BTCS-307 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Knowledge Representation, Machine Learning Fundamentals, Natural Language Processing Basics |
| BTAI-301 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Introduction, Model Evaluation and Selection, Ensemble Methods |
| BTCS-351 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Protocol Implementation, Network Monitoring Tools, Network Security Basics |
| BTCS-353 | Software Engineering Lab | Lab | 1 | UML Diagram Tools, Version Control Systems, Automated Testing Frameworks, Project Management Tools, Software Documentation |
| BTAI-351 | Machine Learning Lab | Lab | 1 | Scikit-learn Implementation, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning, Data Visualization for ML |
| BTCS-355 | Mini Project-I | Project | 2 | Problem Identification, System Design, Implementation and Testing, Project Report Writing, Presentation Skills |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCS-302 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| BTCS-304 | Microprocessor & Microcontroller | Core | 3 | 8085/8086 Architecture, Assembly Language Programming, Interrupts and DMA, Memory Interfacing, I/O Interfacing and Microcontrollers |
| BTAI-302 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow/Keras) |
| BTAI-304 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Word Embeddings, Syntactic and Semantic Analysis, Machine Translation |
| BTAI-306 | Computer Vision | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| BTAI-308 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning and SARSA, Policy Gradient Algorithms |
| BTAI-310 | Big Data Analytics | Elective | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce, Spark, Data Warehousing and Data Mining |
| BTHU-301 (Open Elective-I) | Open Elective-I | Elective | 3 | Interdisciplinary Studies, Humanities and Social Sciences, Management Principles, Environmental Ethics, Financial Literacy |
| BTCS-352 | Compiler Design Lab | Lab | 1 | Lexical Analyzer Implementation, Parser Design (LL, LR), Syntax Directed Translation, Intermediate Code Generation, Compiler Tools (Lex/Yacc) |
| BTCS-354 | Microprocessor & Microcontroller Lab | Lab | 1 | Assembly Language Programming, Interfacing Peripherals, Timer/Counter Programming, ADC/DAC Interface, Microcontroller Projects |
| BTAI-352 | Deep Learning Lab | Lab | 1 | TensorFlow/Keras Implementation, CNNs for Image Recognition, RNNs for Sequence Data, Generative Adversarial Networks (GANs), Transfer Learning Techniques |
| BTCS-360 | Industrial Training (6 Weeks) | Project | 2 | Industry Project Execution, Technical Report Writing, Presentation Skills, Professional Skill Enhancement, Problem Solving in Real-World Context |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTCS-401 | Cryptography & Network Security | Core | 3 | Cryptographic Algorithms, Network Security Protocols, Authentication and Authorization, Firewalls and IDS/IPS, Malware and Cyber Attacks |
| BTAI-401 | AI in Robotics | Core | 3 | Robot Kinematics, Robot Control, Sensors and Actuators, Motion Planning, Vision for Robotics, Machine Learning in Robotics |
| BTAI-403 | Advanced Machine Learning | Elective | 3 | Support Vector Machines, Kernel Methods, Dimensionality Reduction, Graphical Models, Bayesian Learning |
| BTAI-405 | Expert Systems | Elective | 3 | Knowledge Engineering, Inference Engines, Rule-Based Systems, Uncertainty Management, Fuzzy Logic |
| BTAI-407 | Computer Graphics | Elective | 3 | Rasterization Algorithms, Geometric Transformations, Viewing Pipeline, Shading and Lighting Models, Texture Mapping |
| BTAI-409 | Big Data Visualization | Elective | 3 | Data Storytelling, Dashboard Design, Tableau, D3.js, Geospatial Visualization |
| BTAI-411 | Edge AI | Elective | 3 | Edge Computing Architectures, On-device Machine Learning, TinyML, Federated Learning, Edge Security |
| BTAI-413 | Blockchain Technology | Elective | 3 | Cryptographic Hashes, Distributed Ledgers, Smart Contracts, Consensus Mechanisms, Blockchain Applications |
| BTHU-401 (Open Elective-II) | Open Elective-II | Elective | 3 | Project Management, Entrepreneurship Development, Intellectual Property Rights, Organizational Behavior, Indian Economic Environment |
| BTCS-451 | Cryptography & Network Security Lab | Lab | 1 | Symmetric Key Cryptography Implementation, Asymmetric Key Cryptography, Digital Signatures, Firewall Configuration, Intrusion Detection Systems |
| BTAI-451 | AI in Robotics Lab | Lab | 1 | Robot Simulation (ROS), Sensor Integration, Actuator Control, Path Planning Algorithms, Vision Systems for Robotics |
| BTAI-453 | Project-I | Project | 3 | Problem Definition, Literature Review, System Design and Architecture, Implementation and Testing, Report Writing and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-402 | Quantum Computing for AI | Elective | 3 | Quantum Gates and Circuits, Superposition and Entanglement, Quantum Algorithms (Grover''''s, Shor''''s), Quantum Machine Learning, Quantum Computing Platforms |
| BTAI-404 | Speech Recognition | Elective | 3 | Phonetics and Phonology, Acoustic Modeling (HMMs), Language Modeling, Deep Learning for Speech, Speech Synthesis |
| BTAI-406 | Recommender Systems | Elective | 3 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommenders, Evaluation Metrics, Cold Start Problem |
| BTAI-408 | Bio-Inspired AI | Elective | 3 | Swarm Intelligence, Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Artificial Immune Systems |
| BTAI-410 | Ethical AI & Explainable AI | Elective | 3 | AI Ethics Principles, Bias and Fairness in AI, Transparency and Accountability, Interpretability Methods (LIME, SHAP), Responsible AI Development |
| BTAI-412 | AI for Cyber Security | Elective | 3 | Anomaly Detection, Malware Analysis with AI, Threat Intelligence, Security Analytics, Adversarial AI Attacks and Defenses |
| BTAI-452 | Project-II / Dissertation | Project | 4 | Advanced Research Methodology, Prototype Development, Extensive Testing and Validation, Thesis Writing and Defense, Innovation and Industry Problem Solving |
