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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 Artificial Intelligence & Machine Learning program at Shoolini University focuses on equipping students with expertise in intelligent systems, data-driven decision making, and advanced computational techniques. It addresses the growing need for skilled AI professionals across India''''s burgeoning tech industry, integrating theoretical knowledge with practical applications to foster innovation and problem-solving capabilities.
Who Should Apply?
This program is ideal for fresh graduates from 10+2 with a strong aptitude in Mathematics and Science, seeking entry into the high-demand AI/ML sector. It also caters to aspiring researchers and innovators who wish to contribute to cutting-edge technological advancements, preparing them for roles requiring analytical thinking and complex problem-solving skills in data science, automation, and intelligent systems development.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, and Robotics Engineers in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA in top Indian companies and MNCs. The program fosters growth trajectories into lead architect or research roles, aligning with industry certifications like TensorFlow Developer or AWS Machine Learning Specialty.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with Competitive Coding- (Semester 1-2)
Dedicate time to consistent practice of C/C++ or Python programming, focusing on data structures and algorithms. Participate in online competitive programming platforms to build logical thinking and problem-solving speed, crucial for all advanced AI/ML concepts.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks
Career Connection
Strong coding skills are the bedrock for any tech role, directly impacting selection in campus placements for software development and AI engineering positions.
Build a Robust Mathematical & Statistical Foundation- (Semester 1-3)
Focus intensely on engineering mathematics, discrete mathematics, and probability & statistics. Utilize online courses and textbooks to solidify concepts, as these form the theoretical backbone for understanding complex AI/ML algorithms.
Tools & Resources
Khan Academy, NPTEL lectures, MIT OpenCourseware
Career Connection
A strong mathematical background is essential for understanding algorithm mechanics and research in AI, opening doors to advanced R&D roles and higher studies.
Engage in Interdisciplinary Project-Based Learning- (Semester 1-2)
Actively seek out opportunities for small projects that combine engineering physics, basic electricals, and early programming. Collaborate with peers to apply theoretical knowledge to tangible outcomes, even simple ones.
Tools & Resources
Arduino/Raspberry Pi kits, GitHub, Local Hackathons
Career Connection
Early project experience showcases initiative and practical application skills to recruiters, differentiating you from peers and building a strong portfolio.
Intermediate Stage
Develop Practical AI/ML Skills with Industry Tools- (Semester 3-5)
Go beyond lab exercises; build personal projects using Python libraries like TensorFlow, PyTorch, and Scikit-learn. Focus on real-world datasets and problems, documenting your process and results on platforms like GitHub.
Tools & Resources
Kaggle, Google Colab, GitHub, TensorFlow/PyTorch documentation
Career Connection
Hands-on experience with industry-standard tools is vital for securing internships and entry-level AI/ML engineering roles, demonstrating job-readiness.
Network with Professionals and Participate in Workshops- (Semester 4-6)
Attend industry workshops, webinars, and conferences (both online and offline) related to AI, ML, and Data Science. Connect with professionals on LinkedIn and seek mentorship or advice to understand industry trends and job market expectations.
Tools & Resources
LinkedIn, Meetup groups, Coursera/Udemy workshops, Shoolini University Alumni Network
Career Connection
Networking often leads to internship and job opportunities, as well as valuable insights into career paths and skill requirements from experienced professionals.
Contribute to Open Source Projects and Research Papers- (Semester 4-6)
Look for beginner-friendly open-source projects in AI/ML on GitHub or contribute to departmental research under faculty guidance. This provides exposure to collaborative development and academic rigor.
Tools & Resources
GitHub, arXiv.org (for reading), Faculty mentors
Career Connection
Open-source contributions and research experience significantly boost your resume, appealing to companies seeking innovative problem-solvers and R&D roles.
Advanced Stage
Undertake Specialization-Focused Capstone Projects- (Semester 7-8)
For your major projects, choose complex, real-world problems aligned with your specific AI/ML interests (e.g., NLP, Computer Vision, Reinforcement Learning). Work in teams, mimicking industry project cycles from ideation to deployment.
Tools & Resources
Jira/Trello for project management, Cloud platforms (AWS/Azure/GCP), Docker/Kubernetes
Career Connection
High-impact capstone projects serve as powerful portfolio pieces, directly demonstrating your ability to deliver end-to-end solutions to potential employers.
Secure and Excel in Industry Internships- (Semester 6-8)
Actively pursue long-duration internships (6+ months if possible) in relevant AI/ML roles. Treat the internship as an extended interview, demonstrating strong work ethic, technical skills, and cultural fit, aiming for a Pre-Placement Offer (PPO).
Tools & Resources
University Placement Cell, Internshala, Company career pages
Career Connection
Internships are the most direct path to full-time employment, offering invaluable industry exposure and often leading to direct job offers upon graduation.
Prepare for Technical & Behavioral Interviews Rigorously- (Semester 7-8)
Practice coding challenges, brush up on core AI/ML concepts, and prepare for behavioral questions. Conduct mock interviews, focusing on explaining your project work, understanding algorithms, and demonstrating problem-solving approaches clearly.
Tools & Resources
LeetCode, Educative.io, Mock interview platforms, Career Services at Shoolini
Career Connection
Comprehensive interview preparation is crucial for converting internship offers or securing placements in highly competitive AI/ML roles across various tech companies.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, Mathematics & English with minimum 60% marks in aggregate.
Duration: 4 years (8 semesters)
Credits: 175 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-101 | Engineering Physics | Core | 4 | Wave Motion & Optics, Special Relativity, Quantum Mechanics, Statistical Mechanics, Crystal Structures, Material Properties (Magnetic, Superconductivity, Dielectric), Lasers & Optical Fibres |
| BTAI-102 | Applied Chemistry | Core | 4 | Atomic & Molecular Structure, Thermodynamics & Kinetics, Electrochemistry, Organic Chemistry, Corrosion & Polymers, Environmental Chemistry & Water Treatment |
| BTAI-103 | Basic Electrical Engineering | Core | 4 | DC & AC Circuits, Three-Phase Systems, Transformers & DC Machines, AC Machines, Electrical Measuring Instruments |
| BTAI-104 | Programming for Problem Solving | Core | 3 | Introduction to C, Operators & Expressions, Control Structures, Functions & Arrays, Pointers & Strings, Structures, Unions & Files |
| BTAI-105 | English for Communication | Core | 2 | Basic Grammar & Vocabulary, Comprehension & Writing Skills, Formal Communication, Presentation Skills, Group Discussion, Interviews |
| BTAI-106 | Engineering Physics Lab | Lab | 1 | Experiments on Oscillations, Optics, Electricity, Magnetic Fields, Semiconductors |
| BTAI-107 | Applied Chemistry Lab | Lab | 1 | Volumetric Analysis, Water Quality, Polymer Analysis, Corrosion Studies, Fuel Analysis |
| BTAI-108 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, Measurement of Electrical Quantities, Performance of Motors and Transformers |
| BTAI-109 | Programming for Problem Solving Lab | Lab | 2 | Implementation of C Programs, Control Structures, Functions, Arrays, Pointers, Structures |
| BTAI-110 | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Views, Sectional Views, Development of Surfaces, Auto-CAD Introduction |
| BTAI-111 | Yoga | Elective | 1 | Asanas, Pranayama, Meditation, Stress Management, Yoga Philosophy |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-201 | Engineering Mathematics – I | Core | 4 | Differential Equations (First & Second Order), Laplace Transforms, Series Solutions, Fourier Series, Partial Differential Equations |
| BTAI-202 | Database Management System | Core | 3 | DBMS Introduction, ER Model, Relational Model, SQL, Normalization, Transaction & Concurrency Control |
| BTAI-203 | Data Structures & Algorithms | Core | 3 | Arrays & Linked Lists, Stacks & Queues, Trees, Graph Data Structures, Sorting Algorithms, Searching Algorithms |
| BTAI-204 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide & Conquer, Greedy Algorithms, Dynamic Programming, Backtracking & Branch and Bound, NP-Completeness |
| BTAI-205 | Cyber Security | Core | 3 | Introduction to Cyber Security, Cryptography, Network Security, Web Security, Cyber Forensics, Cyber Laws & Ethics |
| BTAI-206 | Constitution of India | Mandatory Non-Credit | 2 | Constitutional Framework, Fundamental Rights & Duties, Directive Principles, Union & State Government, Judiciary, Local Self-Government |
| BTAI-207 | Universal Human Values & Ethics | Mandatory Non-Credit | 2 | Introduction to Value Education, Human Values, Professional Ethics, Harmony in Family & Society, Co-existence with Nature |
| BTAI-208 | Database Management System Lab | Lab | 1 | SQL Queries (DDL, DML, DCL), Joins, Views, Stored Procedures, Triggers, Database Connectivity |
| BTAI-209 | Data Structures & Algorithms Lab | Lab | 2 | Implementation of Stacks, Queues, Linked Lists, Trees, Graphs, Sorting & Searching Algorithms |
| BTAI-210 | Environmental Science | Mandatory Non-Credit | 2 | Natural Resources, Ecosystems, Biodiversity, Environmental Pollution, Social Issues & Environment, Human Population & Environment |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-301 | Engineering Mathematics – II | Core | 4 | Vector Calculus, Matrix Theory, Probability & Statistics, Complex Analysis, Numerical Methods |
| BTAI-302 | Discrete Mathematics | Core | 4 | Set Theory, Logic & Proofs, Relations & Functions, Graph Theory, Combinatorics, Algebraic Structures |
| BTAI-303 | Object Oriented Programming | Core | 3 | OOP Concepts (Classes, Objects, Encapsulation, Abstraction), Inheritance, Polymorphism, Exception Handling, File I/O, Templates |
| BTAI-304 | Operating System | Core | 3 | Introduction to OS, Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems, I/O Systems |
| BTAI-305 | Digital Logic Design | Core | 3 | Boolean Algebra & Logic Gates, Combinational Circuits, Sequential Circuits, Registers & Counters, Memory Units, Programmable Logic Devices |
| BTAI-306 | Object Oriented Programming Lab | Lab | 2 | C++/Java Programming, Classes & Objects, Inheritance & Polymorphism, Operator Overloading, Exception Handling, GUI Programming |
| BTAI-307 | Operating System Lab | Lab | 1 | Unix/Linux Commands, Shell Scripting, Process Management, Thread Programming, Inter-process Communication |
| BTAI-308 | Digital Logic Design Lab | Lab | 1 | Implementation of Logic Gates, Adders/Subtractors, Encoders/Decoders, Flip-Flops, Counters, MUX/DEMUX |
| BTAI-309 | Industry Internship/Project | Project | 1 | Project Identification, Problem Definition, Literature Review, Methodology, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-401 | Computer Organization & Architecture | Core | 4 | Basic Computer Organization, CPU Design, Memory Organization, I/O Organization, Control Unit Design, Pipelining & Parallel Processing |
| BTAI-402 | Automata Theory & Compiler Design | Core | 4 | Finite Automata & Regular Expressions, Context-Free Grammars & Pushdown Automata, Turing Machines, Lexical Analysis, Syntax Analysis, Semantic Analysis |
| BTAI-403 | Artificial Intelligence | Core | 3 | Introduction to AI, Intelligent Agents, Problem Solving (Search Strategies), Knowledge Representation & Reasoning, Expert Systems, Game Playing |
| BTAI-404 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning, Model Evaluation, Ensemble Methods, Bias-Variance Tradeoff |
| BTAI-405 | Web Technologies | Core | 3 | HTML, CSS, JavaScript, DOM Manipulation, XML, AJAX, Client-side Scripting, Server-side Technologies (Basics) |
| BTAI-406 | Artificial Intelligence Lab | Lab | 2 | Python Programming for AI, Implementation of Search Algorithms, Heuristic Search, Logic Programming (Prolog/LISP) |
| BTAI-407 | Machine Learning Lab | Lab | 2 | Python Libraries (NumPy, Pandas, Scikit-learn), Data Preprocessing, Implementing Regression/Classification Models, Cross-Validation, Visualization |
| BTAI-408 | Web Technologies Lab | Lab | 1 | HTML/CSS Website Design, JavaScript Forms, DOM Manipulation Projects, Basic Server-Side Interaction |
| BTAI-409 | AI/ML Based Project | Project | 1 | Project Scope, Requirement Gathering, Design & Development, Testing, Documentation, Presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-501 | Compiler Design | Core | 4 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Error Handling & Recovery |
| BTAI-502 | Software Engineering | Core | 4 | Software Process Models, Requirements Engineering, Software Design (Architectural, Detailed), Software Testing, Software Project Management, Software Quality Assurance |
| BTAI-503 | Deep Learning | Core | 3 | Neural Network Fundamentals, Feedforward Networks, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| BTAI-504 | Computer Vision | Core | 3 | Image Representation, Image Preprocessing, Feature Extraction, Image Segmentation, Object Detection, Image Classification, Video Processing |
| BTAI-505 | Open Elective – I | Elective | 3 | |
| BTAI-506 | Deep Learning Lab | Lab | 2 | Implementation of ANN, CNN, RNN models, Hyperparameter Tuning, Transfer Learning, Image Recognition Projects, Text Generation |
| BTAI-507 | Computer Vision Lab | Lab | 2 | Image Processing with OpenCV, Edge Detection, Feature Matching, Object Detection, Face Recognition, Augmented Reality Basics |
| BTAI-508 | Professional Ethics & Values | Core | 2 | Ethical Dilemmas, Codes of Ethics, Intellectual Property Rights, Privacy & Data Security, Whistle-blowing, Social & Environmental Ethics |
| BTAI-509 | Minor Project | Project | 1 | Problem Definition, System Design, Implementation, Testing, Project Report |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-601 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Machine Translation, Text Summarization |
| BTAI-602 | Cloud Computing | Core | 3 | Cloud Architecture, Virtualization, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Cloud Storage, Cloud Security |
| BTAI-603 | Data Analytics | Core | 3 | Data Collection & Cleaning, Exploratory Data Analysis, Statistical Hypothesis Testing, Predictive Modeling, Data Visualization, Business Intelligence |
| BTAI-604 | Elective – I | Elective | 3 | |
| BTAI-605 | Open Elective – II | Elective | 3 | |
| BTAI-606 | Natural Language Processing Lab | Lab | 2 | NLTK, spaCy, Text Preprocessing, Tokenization, Building Chatbots, Sentiment Analysis Application |
| BTAI-607 | Cloud Computing Lab | Lab | 1 | AWS/Azure/GCP Basics, EC2 Instances, S3 Storage, Load Balancing, Serverless Functions, Database Services |
| BTAI-608 | Industrial Training | Internship | 1 | On-site Project Work, Industry Best Practices, Professional Skill Development, Report Submission |
| BTAI-609 | Project Viva-Voce | Project | 1 | Project Presentation, Technical Explanation, Q&A Session, Evaluation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-701 | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark, NoSQL Databases, Data Streaming, Big Data Security |
| BTAI-702 | Elective – II | Elective | 3 | |
| BTAI-703 | Elective – III | Elective | 3 | |
| BTAI-704 | Elective – IV | Elective | 3 | |
| BTAI-705 | Major Project – I | Project | 4 | Project Proposal, Literature Survey, System Design, Module Development, Progress Reporting |
| BTAI-706 | Seminar | Project | 1 | Technical Presentation, Research Paper Analysis, Discussion on Emerging Technologies |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTAI-801 | Major Project – II | Project | 12 | Final System Implementation, Extensive Testing, Performance Evaluation, Comprehensive Documentation, Final Project Defense |
| BTAI-802 | Industry Training / Internship | Internship | 4 | Advanced Skill Application, Real-World Problem Solving, Professional Networking, Career Development |
