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M-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 M.Tech Artificial Intelligence program at Parul Institute of Engineering & Technology focuses on equipping students with advanced theoretical and practical knowledge in AI, Machine Learning, Deep Learning, NLP, and Computer Vision. The curriculum is designed to meet the burgeoning demand for AI professionals in India''''s rapidly growing tech sector, emphasizing practical application and research-driven innovation. Graduates are prepared for cutting-edge roles in various industries.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech in Computer Science, IT, AI, Data Science, or related fields) seeking specialized expertise in Artificial Intelligence. It caters to fresh graduates aiming for impactful entry-level AI roles and working professionals looking to upskill or transition into advanced AI development, research, or data science positions within the dynamic Indian job market.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths as AI Engineers, Machine Learning Engineers, Data Scientists, Deep Learning Specialists, or AI Researchers in India. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning upwards of INR 15-30 LPA in leading tech companies and startups. The program aligns with industry needs, fostering skills crucial for rapid professional growth and innovation.
Student Success Practices
Foundation Stage
Master Mathematical and Algorithmic Foundations- (Semester 1-2)
Dedicate significant time in Semesters 1 and 2 to build a strong base in linear algebra, probability, calculus, and advanced data structures/algorithms. These are the bedrock of AI. Actively solve problems and engage with the faculty for conceptual clarity.
Tools & Resources
NPTEL courses on Linear Algebra and Probability, HackerRank/LeetCode for algorithm practice, Textbooks like ''''Deep Learning'''' by Goodfellow et al.
Career Connection
A solid foundation is critical for understanding complex AI models, designing efficient algorithms, and excelling in technical interviews for AI/ML engineering roles.
Engage in Hands-on AI Programming- (Semester 1-2)
Translate theoretical knowledge into practical skills by consistently coding. Focus on Python, its AI/ML libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), and development environments. Participate in coding competitions and practical lab sessions rigorously.
Tools & Resources
Kaggle (for datasets and competitions), Google Colab/Jupyter Notebooks, Official documentation for TensorFlow/PyTorch, GeeksforGeeks for coding challenges
Career Connection
Proficiency in AI programming and tools is non-negotiable for any AI/ML role, enabling rapid prototyping, model development, and system implementation in industry.
Initiate Research and Academic Exploration- (Semester 1-2)
Beyond coursework, explore research papers in areas of interest within AI. Attend department seminars, workshops, and potentially assist faculty with ongoing research projects. This fosters a research mindset early on and helps identify specialization areas.
Tools & Resources
arXiv.org, Google Scholar, ResearchGate, Departmental research groups and faculty mentors
Career Connection
Early research exposure enhances critical thinking, problem-solving, and communication skills, vital for M.Tech dissertation and potential R&D roles in AI.
Intermediate Stage
Advanced Stage
Undertake Impactful Dissertation Research- (Semester 3-4)
In Semesters 3 and 4, dedicate thoroughly to your Dissertation Phase I and II. Choose a relevant and challenging problem, conduct rigorous research, implement novel solutions, and aim for quality publications. Collaborate with peers and faculty advisors for guidance.
Tools & Resources
Academic journals (IEEE, ACM), Dissertation templates, Plagiarism checker tools, University research labs and faculty expertise
Career Connection
A strong dissertation demonstrates advanced problem-solving, research capabilities, and specialization, significantly boosting prospects for R&D, academia, and high-level industry positions.
Secure and Leverage Industrial Internships- (Semester 3)
Actively seek out and complete a meaningful internship or industrial training in Semester 3. Focus on applying AI concepts to real-world business problems within a company. This experience provides invaluable industry exposure, builds professional networks, and can lead to pre-placement offers.
Tools & Resources
LinkedIn Jobs, Internshala, University career services, Company career pages for internships
Career Connection
Internships are crucial for bridging the gap between academia and industry, offering practical experience that is highly valued by recruiters and often converts into full-time employment.
Prepare Strategically for Placements and Career Entry- (Semester 3-4)
Beyond technical skills, focus on developing soft skills like communication, presentation, and teamwork. Prepare for interviews by practicing technical questions, aptitude tests, and mock interviews. Tailor your resume and portfolio to highlight AI projects and research work.
Tools & Resources
Mock interview platforms, Resume building workshops, Networking events, Interview preparation guides for AI/ML roles
Career Connection
Comprehensive preparation ensures you are interview-ready and can effectively showcase your skills and knowledge to potential employers, leading to successful placements in top AI companies.
Program Structure and Curriculum
Eligibility:
- Passed B.E./B.Tech. in Computer Engineering/Information Technology/Computer Science & Engineering/Computer Engineering (Software Engineering) / Information & Communication Technology / Artificial Intelligence / Artificial Intelligence & Machine Learning / Data Science or equivalent as recognized by the University with minimum 50% Marks (45% for reserved category) in qualifying examination.
Duration: 4 semesters / 2 years
Credits: 80 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 08.19101 | Mathematical Foundations of Artificial Intelligence | Core | 4 | Linear Algebra for AI, Probability and Statistics for AI, Optimization Techniques, Calculus for Machine Learning, Discrete Mathematics for AI |
| 08.19102 | Advanced Machine Learning | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Ensemble Methods, Introduction to Reinforcement Learning, Feature Engineering and Selection |
| 08.19103 | Data Structures and Algorithms for AI | Core | 4 | Advanced Data Structures (Trees, Graphs), Algorithm Design Paradigms, Graph Algorithms, Dynamic Programming, Computational Complexity Analysis |
| 08.19104 | Artificial Intelligence Lab – I | Lab | 2 | Python Programming for AI, Machine Learning Libraries (Scikit-learn), Data Preprocessing and Visualization, Model Training and Evaluation, Basic AI Algorithm Implementation |
| 08.19105 | Research Methodology and IPR | Core | 3 | Formulating Research Problem, Research Design and Methods, Data Collection and Analysis, Technical Report Writing, Intellectual Property Rights |
| 08.19106 | Elective – I | Elective | 3 | 08.19106A: Cognitive Computing, 08.19106B: Big Data Analytics, 08.19106C: Human Computer Interaction |
| 08.19107 | Audit Course – I | Audit | 0 | 08.19107A: English for Research Paper Writing, 08.19107B: Disaster Management, 08.19107C: Sanskrit for Technical Knowledge, 08.19107D: Value Education, 08.19107E: Constitution of India, 08.19107F: Pedagogy Studies, 08.19107G: Stress Management by Yoga, 08.19107H: Personality Development Through Indian Culture |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 08.19201 | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Architectures |
| 08.19202 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Language Models (N-grams, Transformers), Word Embeddings (Word2Vec, BERT), Syntactic and Semantic Analysis, Information Extraction and Sentiment Analysis |
| 08.19203 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition and Detection, Image Segmentation, Deep Learning for Computer Vision |
| 08.19204 | Artificial Intelligence Lab – II | Lab | 2 | Deep Learning Frameworks (TensorFlow, PyTorch), NLP Libraries (NLTK, SpaCy), Computer Vision Libraries (OpenCV), Advanced Model Development, AI Project Implementation |
| 08.19205 | Elective – II | Elective | 3 | 08.19205A: Robotics and AI, 08.19205B: Reinforcement Learning, 08.19205C: Explainable AI |
| 08.19206 | Open Elective – I | Open Elective | 3 | 08.19206A: Business Analytics, 08.19206B: Industrial Safety, 08.19206C: Operations Research, 08.19206D: Cost Management of Engineering Projects, 08.19206E: Composite Materials, 08.19206F: Waste to Energy |
| 08.19207 | Audit Course – II | Audit | 0 | 08.19107A: English for Research Paper Writing, 08.19107B: Disaster Management, 08.19107C: Sanskrit for Technical Knowledge, 08.19107D: Value Education, 08.19107E: Constitution of India, 08.19107F: Pedagogy Studies, 08.19107G: Stress Management by Yoga, 08.19107H: Personality Development Through Indian Culture |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 08.19301 | Dissertation Phase – I | Project | 10 | Extensive Literature Review, Problem Identification and Formulation, Research Gap Analysis, Methodology Design, Preliminary Data Collection |
| 08.19302 | Internship / Project (Industrial Training) | Internship/Project | 6 | Industry-Specific Skill Application, Real-world Problem Solving, Professional Communication, Teamwork and Project Management, Industrial Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 08.19401 | Dissertation Phase – II | Project | 16 | Advanced Research Experimentation, Data Analysis and Interpretation, Thesis Writing and Documentation, Scientific Paper Publication, Dissertation Defense and Viva |
