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B-TECH in Artificial Intelligence at Parul Institute of Engineering & Technology
Vadodara, Gujarat
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About the Specialization
What is Artificial Intelligence at Parul Institute of Engineering & Technology Vadodara?
This Artificial Intelligence program at Parul Institute of Engineering & Technology focuses on developing a strong foundation in AI principles, machine learning, deep learning, natural language processing, and computer vision. It emphasizes practical application and innovation, addressing the rapidly growing demand for AI professionals in the Indian industry. The curriculum is designed to equip students with cutting-edge skills for various AI domains.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming, aspiring to build a career in AI. It also caters to individuals looking to upskill or career changers from related IT fields seeking to transition into the exciting world of artificial intelligence and machine learning in India''''s booming tech sector.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or NLP Specialists in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The program prepares students for industry roles in AI product development, research, and data-driven decision-making within Indian tech companies and global MNCs.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python/C/Java)- (Semester 1-2)
Dedicate significant time to hands-on coding practice in C, Java, and especially Python. Utilize online platforms like HackerRank, LeetCode, and GeeksforGeeks to solve diverse programming problems, focusing on data structures and algorithms, which are crucial for AI. Building strong logic and problem-solving skills early is fundamental.
Tools & Resources
Python (Anaconda distribution), Java Development Kit, C/C++ compiler, HackerRank, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong programming proficiency is the bedrock for any AI role, directly impacting your ability to implement algorithms, develop models, and clear technical interviews for internships and placements in leading tech firms.
Build a Solid Mathematical & Statistical Base- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics courses. These are the theoretical underpinnings of AI and ML. Supplement classroom learning with resources like Khan Academy, NPTEL courses, and specialized textbooks. Understand the ''''why'''' behind the algorithms, not just the ''''how''''.
Tools & Resources
Khan Academy (Calculus, Linear Algebra), NPTEL videos (Probability, Statistics), MIT OpenCourseWare, Relevant textbooks
Career Connection
A robust mathematical foundation helps in comprehending complex AI models, debugging them effectively, and contributing to research or advanced development roles, providing a competitive edge in specialized AI jobs.
Engage in Peer Learning & Group Projects- (Semester 1-2)
Form study groups to discuss concepts, clarify doubts, and work on small projects together. Collaborative learning fosters deeper understanding and improves team working skills. Participating in college-level coding contests and hackathons with your team can further enhance these skills.
Tools & Resources
Microsoft Teams/Google Meet for discussions, GitHub for collaborative coding, College coding clubs
Career Connection
Teamwork and communication skills are highly valued in the industry. Collaborative project experience demonstrates your ability to work in a professional environment, crucial for project-based roles and startups.
Intermediate Stage
Undertake Mini-Projects & Online Certifications- (Semester 3-5)
Apply theoretical knowledge by undertaking self-initiated mini-projects focusing on specific AI domains like supervised learning, data analytics, or basic NLP. Complement this with industry-recognized online certifications from platforms like Coursera, edX, or Google AI to gain practical skills and a verifiable credential.
Tools & Resources
Kaggle for datasets, Google Colab, Coursera (Andrew Ng''''s ML course), edX (IBM AI Engineering)
Career Connection
These projects and certifications showcase your practical application skills to recruiters and help you build a portfolio, making you a more attractive candidate for internships and entry-level positions in AI/ML.
Participate in AI/ML Competitions & Hackathons- (Semester 3-5)
Actively participate in national and international AI/ML competitions on platforms like Kaggle, DrivenData, and internal college hackathons. This provides real-world problem-solving experience, allows you to learn from peers, and offers opportunities to apply advanced techniques beyond classroom curriculum.
Tools & Resources
Kaggle, DrivenData, GitHub for solutions, College Hackathon platforms
Career Connection
Winning or performing well in these competitions significantly boosts your resume, demonstrating practical expertise, resilience, and a competitive spirit, which are highly regarded by top tech companies and startups.
Build a Professional Network & Seek Mentorship- (Semester 3-5)
Attend industry workshops, webinars, and conferences (both online and offline) focused on AI in India. Connect with professionals on LinkedIn, seek guidance from faculty, and try to find mentors who can offer insights into career paths, job market trends, and advanced topics. Networking can open doors to internships and job opportunities.
Tools & Resources
LinkedIn, Professional AI communities, Departmental seminars, Faculty advisors
Career Connection
Networking is vital for career growth in India. It helps you discover hidden job markets, get referrals, and gain valuable industry insights that accelerate your professional development and placement success.
Advanced Stage
Focus on Specialization and Advanced Projects- (Semester 6-8)
As you enter advanced semesters, identify a specific area of AI (e.g., NLP, Computer Vision, Reinforcement Learning) that interests you. Work on significant projects, potentially as part of your final year project, applying advanced techniques and contributing to open-source initiatives. Publish your work on GitHub and consider research paper submissions.
Tools & Resources
TensorFlow/PyTorch, OpenCV, NLTK/SpaCy, GitHub, arXiv for research papers
Career Connection
Specialized projects demonstrate deep expertise and passion, making you a strong candidate for niche AI roles, research positions, or even entrepreneurial ventures in the Indian tech ecosystem.
Pursue Internships & Industrial Training- (Semester 6-7)
Secure internships with reputable companies or AI startups in India. Prioritize roles that offer hands-on experience with real-world datasets and projects. An internship is often the most direct path to a full-time job offer and provides invaluable practical exposure to industry workflows and tools.
Tools & Resources
Internshala, Naukri.com, LinkedIn Jobs, College placement cell
Career Connection
Internships are critical for placement. They provide practical skills, industry contacts, and often lead to pre-placement offers (PPOs) from companies, ensuring a smoother transition from academic life to a professional career in India.
Prepare for Placements with Mock Interviews and Case Studies- (Semester 7-8)
Systematically prepare for campus placements by practicing technical questions, aptitude tests, and HR interviews. Engage in mock interview sessions, solve AI/ML case studies, and refine your resume and portfolio. Focus on articulating your project experiences and understanding of AI concepts clearly.
Tools & Resources
Glassdoor, GeeksforGeeks (Interview Prep), LeetCode (for coding rounds), Company-specific interview guides
Career Connection
Effective placement preparation is essential for securing your desired job. A well-prepared candidate stands out in competitive campus recruitment drives, maximizing the chances of landing a high-paying AI role in India.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics and Mathematics as compulsory subjects along with Chemistry/Biotechnology/Biology/Technical Vocational as one of the subjects and minimum 45% marks (40% for reserved category) in aggregate or relevant equivalent qualification.
Duration: 8 semesters / 4 years
Credits: 164 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203101101 | Calculus | Core Theory | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Sequences and Series, Vector Calculus |
| 203101102 | Basic Electrical Engineering | Core Theory | 4 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Power Systems |
| 203101103 | Programming for Problem Solving | Core Theory | 3 | Programming Fundamentals, Control Flow, Functions, Arrays and Pointers, Structures and File I/O |
| 203101104 | Engineering Graphics & Design | Core Theory | 2 | Introduction to Engineering Graphics, Orthographic Projections, Isometric Projections, Sectional Views, Computer Aided Drafting |
| 203101105 | English | Core Theory | 2 | Functional Grammar, Communication Skills, Report Writing, Presentation Skills, Reading Comprehension |
| 203101106 | Programming for Problem Solving Lab | Lab | 2 | C Programming Practice, Debugging Techniques, Problem Solving with C, Algorithmic Implementation, Data Structure Basics |
| 203101107 | Basic Electrical Engineering Lab | Lab | 1 | Circuit Laws Verification, AC/DC Circuit Analysis, Transformer Characteristics, Motor Control Experiments, Power Factor Improvement |
| 203101108 | Engineering Graphics & Design Lab | Lab | 1 | 2D Drawing Exercises, 3D Modeling Software, Assembly Drawing, CAD Tools Practice, Geometric Dimensioning |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203101201 | Linear Algebra | Core Theory | 4 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, Linear Transformations, Orthogonality and Inner Products |
| 203101202 | Engineering Physics | Core Theory | 3 | Quantum Mechanics, Solid State Physics, Semiconductor Physics, Laser Physics, Fiber Optics |
| 203101203 | Data Structure and Algorithms | Core Theory | 3 | Arrays, Stacks, Queues, Linked Lists, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 203101204 | Environmental Science | Core Theory | 2 | Ecosystems, Biodiversity Conservation, Pollution Control, Sustainable Development, Environmental Policies |
| 203101205 | Object Oriented Programming using JAVA | Core Theory | 3 | OOP Concepts, Java Fundamentals, Inheritance and Polymorphism, Exception Handling, Multithreading |
| 203101206 | Data Structure and Algorithms Lab | Lab | 2 | Implementation of Stacks, Queues, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| 203101207 | Engineering Physics Lab | Lab | 1 | Optical Fiber Experiments, Diode Characteristics, Hall Effect, Diffraction Grating, Semiconductor Device Testing |
| 203101208 | Object Oriented Programming using JAVA Lab | Lab | 2 | Java Programming Practice, Class and Object Implementation, Inheritance Programs, Polymorphism Exercises, GUI Development Basics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112301 | Discrete Mathematics | Core Theory | 4 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Counting and Probability |
| 203112302 | Database Management System | Core Theory | 3 | DBMS Architecture, ER Model, Relational Model, SQL Queries, Normalization |
| 203112303 | Operating System | Core Theory | 3 | OS Structures, Process Management, Memory Management, File Systems, Deadlocks |
| 203112304 | Digital Logic and Design | Core Theory | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory and Programmable Logic |
| 203112305 | Python for AI | Core Theory | 3 | Python Fundamentals, Data Structures in Python, NumPy and Pandas, Data Visualization, File Handling and APIs |
| 203112306 | Database Management System Lab | Lab | 2 | SQL Practice, Database Design, Data Manipulation, Database Connectivity, NoSQL Introduction |
| 203112307 | Operating System Lab | Lab | 2 | Linux Commands, Shell Scripting, Process Scheduling, Memory Allocation, System Calls |
| 203112308 | Digital Logic and Design Lab | Lab | 1 | Logic Gate Implementation, Combinational Circuit Design, Sequential Circuit Design, Flip-Flops and Counters, Multiplexers and Demultiplexers |
| 203112309 | Python for AI Lab | Lab | 1 | Python Programming Practice, NumPy/Pandas Exercises, Data Preprocessing, Basic Machine Learning Libraries, Visualization with Matplotlib |
| 203112310 | Applied Statistics | Core Theory | 2 | Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA, Statistical Inference |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112401 | Design and Analysis of Algorithms | Core Theory | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 203112402 | Computer Organization and Architecture | Core Theory | 3 | CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining, Parallel Processing |
| 203112403 | Artificial Intelligence | Core Theory | 4 | Introduction to AI, Problem Solving Agents, Knowledge Representation, Machine Learning Basics, Natural Language Processing |
| 203112404 | Probability and Stochastic Processes | Core Theory | 4 | Random Variables, Probability Distributions, Stochastic Processes, Markov Chains, Queuing Theory |
| 203112405 | Web Technology | Core Theory | 3 | HTML, CSS, JavaScript, Web Servers, Client-Side Scripting, Server-Side Technologies, Web Security Basics |
| 203112406 | Design and Analysis of Algorithms Lab | Lab | 2 | Algorithm Implementation, Time/Space Complexity Analysis, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithm Solutions |
| 203112407 | Artificial Intelligence Lab | Lab | 2 | AI Search Algorithms, Knowledge Representation Tools, Prolog/LISP Basics, Introduction to ML Libraries, Simple AI Agent Implementation |
| 203112408 | Web Technology Lab | Lab | 1 | HTML/CSS Website Design, JavaScript Interactivity, Server-Side Scripting Practice, Database Integration for Web, Responsive Web Design |
| 203112409 | Universal Human Values | Ability Enhancement Compulsory Course | 2 | Self-Exploration as the Process, Harmony in the Family, Harmony in the Society, Harmony in Nature, Implications of Harmony |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112501 | Machine Learning | Core Theory | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Ensemble Methods |
| 203112502 | Computer Networks | Core Theory | 3 | Network Topologies, OSI and TCP/IP Models, Network Protocols, Routing Algorithms, Network Security Basics |
| 203112503 | Deep Learning | Core Theory | 4 | Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Deep Learning Frameworks |
| 203112504 | Professional Elective I | Elective Theory | 3 | Elective specific topics will vary. |
| 203112505 | Open Elective I | Open Elective Theory | 3 | Elective specific topics will vary. |
| 203112506 | Machine Learning Lab | Lab | 2 | Scikit-learn Practice, Classification Algorithms, Regression Algorithms, Clustering Techniques, Model Hyperparameter Tuning |
| 203112507 | Deep Learning Lab | Lab | 2 | TensorFlow/PyTorch Basics, CNN Implementation, RNN Implementation, Image Recognition Tasks, Sequence Prediction Models |
| 203112508 | Mini Project | Project | 2 | Problem Identification, Design and Development, Testing and Evaluation, Report Writing, Presentation |
| 203112509 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis, Routing Protocols Implementation, Network Simulation Tools |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112601 | Natural Language Processing | Core Theory | 4 | Text Preprocessing, Language Models, Word Embeddings, Sequence to Sequence Models, NLP Applications |
| 203112602 | Big Data Analytics | Core Theory | 3 | Big Data Concepts, Hadoop Ecosystem, Spark Programming, NoSQL Databases, Data Stream Processing |
| 203112603 | Computer Vision | Core Theory | 4 | Image Processing Fundamentals, Feature Detection, Object Recognition, Image Segmentation, Motion Analysis |
| 203112604 | Professional Elective II | Elective Theory | 3 | Elective specific topics will vary. |
| 203112605 | Open Elective II | Open Elective Theory | 3 | Elective specific topics will vary. |
| 203112606 | Natural Language Processing Lab | Lab | 2 | NLTK/SpaCy Practice, Text Classification, Sentiment Analysis, Machine Translation Basics, Chatbot Development |
| 203112607 | Big Data Analytics Lab | Lab | 2 | Hadoop Ecosystem Practice, Spark Data Processing, MapReduce Programming, Hive/Pig Queries, NoSQL Database Management |
| 203112608 | Computer Vision Lab | Lab | 2 | OpenCV Practice, Image Feature Extraction, Object Detection, Image Segmentation Algorithms, Face Recognition |
| 203112609 | Professional Ethics & Values | Ability Enhancement Compulsory Course | 2 | Ethics in Engineering, Ethical Dilemmas, Corporate Social Responsibility, Environmental Ethics, Ethical Hacking and Privacy |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112701 | Reinforcement Learning | Core Theory | 4 | Markov Decision Processes, Q-Learning, SARSA, Deep Reinforcement Learning, Policy Gradient Methods |
| 203112702 | Professional Elective III | Elective Theory | 3 | Elective specific topics will vary. |
| 203112703 | Professional Elective IV | Elective Theory | 3 | Elective specific topics will vary. |
| 203112704 | Open Elective III | Open Elective Theory | 3 | Elective specific topics will vary. |
| 203112705 | Project Phase I | Project | 6 | Problem Definition, Literature Survey, System Design, Prototype Development, Initial Testing |
| 203112706 | Reinforcement Learning Lab | Lab | 2 | RL Environment Setup, Q-Learning Implementation, Deep Q-Networks, Policy Gradient Algorithms, Agent Training and Evaluation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 203112801 | Professional Elective V | Elective Theory | 3 | Elective specific topics will vary. |
| 203112802 | Professional Elective VI | Elective Theory | 3 | Elective specific topics will vary. |
| 203112803 | Project Phase II | Project | 10 | Advanced Implementation, Extensive Testing, Performance Optimization, Documentation, Final Presentation and Viva |
