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M-TECH in Artificial Intelligence And Machine Learning at Symbiosis International University
Pune, Maharashtra
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About the Specialization
What is Artificial Intelligence and Machine Learning at Symbiosis International University Pune?
This Artificial Intelligence and Machine Learning program at Symbiosis International University (through SIT) focuses on equipping students with advanced theoretical knowledge and practical skills in AI/ML, crucial for India''''s rapidly expanding digital economy. The program emphasizes deep learning, data analytics, and ethical AI development, preparing graduates for high-impact roles across various Indian industries. It combines robust academic foundations with applied research.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech.) in relevant disciplines or MCA/M.Sc. degree holders aiming for specialized careers in AI/ML. It caters to fresh graduates seeking entry into cutting-edge AI roles and working professionals looking to upskill or transition into the burgeoning Indian AI sector. A strong analytical background and interest in advanced computing are key prerequisites.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative India-specific career paths as AI Engineers, Machine Learning Scientists, Data Scientists, or Deep Learning Specialists. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals commanding significantly higher packages. The program fosters growth trajectories in R&D, product development, and offers a strong foundation for professional certifications in cloud AI platforms and specialized ML techniques.
Student Success Practices
Foundation Stage
Master Mathematical and Algorithmic Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand the mathematical underpinnings (linear algebra, probability, calculus, optimization) and advanced algorithms. Utilize online platforms for practice problems and competitive programming to strengthen logical thinking. Actively participate in tutorials and doubt-solving sessions.
Tools & Resources
NPTEL courses, Khan Academy, GeeksforGeeks, HackerRank
Career Connection
A strong foundation in these areas is crucial for understanding complex AI/ML models and for excelling in technical interviews for data science and ML engineer roles.
Build a Portfolio of Foundational ML Projects- (Semester 1-2)
Apply theoretical knowledge by working on small, end-to-end machine learning projects. Focus on supervised and unsupervised learning tasks using real-world datasets. Document your code, methodology, and results clearly on platforms like GitHub.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Scikit-learn
Career Connection
Demonstrable projects are essential for showcasing your practical skills to potential employers and differentiate you in the Indian job market.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts, solve problems collaboratively, and prepare for exams. Teach concepts to others to solidify your own understanding. Participate in department-led seminars and technical talks.
Tools & Resources
WhatsApp/Discord groups, College library discussion rooms, YouTube tutorials
Career Connection
Developing strong communication and teamwork skills through peer learning is highly valued in collaborative industry environments.
Intermediate Stage
Specialize and Deepen Knowledge in AI Sub-domains- (Semester 2-3)
Choose electives strategically based on your career interests (e.g., NLP, Computer Vision, Reinforcement Learning). Pursue online specializations or certifications in these areas to gain deeper expertise and a competitive edge.
Tools & Resources
Coursera Specializations, Udacity Nanodegrees, DeepLearning.AI
Career Connection
Specialized skills are highly sought after by companies in India looking for experts in specific AI applications, leading to better job prospects and higher compensation.
Undertake Industry Internships and Live Projects- (Semester 2-3 (Summer breaks))
Actively seek and complete internships with AI/ML teams in companies, startups, or research labs. Work on live industry projects to gain hands-on experience with real data, tools, and project management methodologies. Network with industry professionals.
Tools & Resources
LinkedIn, Internshala, College placement cell, Company career pages
Career Connection
Internships are often a direct pathway to pre-placement offers (PPOs) in India and provide invaluable practical exposure, making you industry-ready.
Participate in AI/ML Hackathons and Competitions- (Semester 2-3)
Regularly participate in hackathons (e.g., conducted by AICTE, industry bodies) and Kaggle competitions. This helps in quick problem-solving, teamwork, and exposure to diverse AI challenges under time constraints.
Tools & Resources
Kaggle, Devfolio, Major League Hacking (MLH) events, College tech fests
Career Connection
Winning or performing well in competitions adds significant weight to your resume and demonstrates your ability to apply AI/ML skills effectively under pressure.
Advanced Stage
Develop a Capstone Project with Real-world Impact- (Semester 3-4)
Focus on your Master''''s project (dissertation) to address a significant problem with a novel AI/ML solution. Aim for publishable quality research or a deployable prototype that can demonstrate clear business value or scientific contribution.
Tools & Resources
University research labs, Industry mentors, Open-source datasets, Cloud computing resources
Career Connection
A strong capstone project is the ultimate demonstration of your expertise, often leading to publication opportunities, job offers, or even startup ventures.
Network Extensively and Attend Conferences- (Semester 3-4)
Actively network with faculty, alumni, and industry leaders through conferences, workshops, and LinkedIn. Attend prominent Indian and international AI/ML conferences to stay updated on trends and identify career opportunities.
Tools & Resources
LinkedIn, AI/ML conferences (e.g., ICLR, NeurIPS, AAAI, local AI meetups), Alumni portals
Career Connection
Networking is vital for discovering hidden job opportunities, mentorship, and building professional relationships that can accelerate your career in India.
Prepare Rigorously for Placements and Interviews- (Semester 3-4)
Refine your resume and LinkedIn profile to highlight AI/ML skills and projects. Practice coding challenges (DSA, ML algorithms) and behavioral questions. Prepare detailed explanations of your projects and research work for technical interviews.
Tools & Resources
LeetCode, Interviews/GeeksforGeeks, Mock interview platforms, Career Services office
Career Connection
Thorough preparation for placement processes significantly increases your chances of securing high-quality job offers from top AI/ML companies in India.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s Degree in Engineering/Technology (B.E./B.Tech.) in Computer Engineering/Information Technology/Electronics & Telecommunication/Electrical/Instrumentation/Mechatronics/Electronics or MCA/M.Sc. (Computer Science/IT/Electronics) with a minimum of 50% marks (45% for SC/ST) at the qualifying examination. A valid GATE score is desirable but not always mandatory for admission.
Duration: 2 years (4 semesters)
Credits: 80 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTCE101 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Trees and Heaps, Graph Algorithms, Dynamic Programming, Amortized Analysis, String Matching Algorithms |
| MTML101 | Mathematical Foundations for Machine Learning | Core | 4 | Linear Algebra for ML, Probability and Statistics, Optimization Techniques, Multivariate Calculus, Information Theory |
| MTML102 | Machine Learning | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Ensemble Methods, Model Evaluation and Selection, Feature Engineering |
| MTCE103 | Research Methodology | Core | 2 | Research Problem Formulation, Literature Review, Research Design and Methods, Data Collection and Analysis, Report Writing and Ethics |
| MTML103 | Machine Learning Lab | Lab | 2 | Python for ML, Data Preprocessing, Scikit-learn Implementations, TensorFlow/PyTorch Basics, Model Training and Evaluation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTML201 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs) |
| MTML202 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Sequence-to-Sequence Models, Language Models (BERT, GPT), Text Classification and Sentiment Analysis |
| MTML203 | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Detection and Description, Object Detection and Recognition, Image Segmentation, Deep Learning for Computer Vision |
| MTML204 | Big Data Analytics | Core | 4 | Hadoop Ecosystem, Apache Spark, NoSQL Databases, Data Warehousing Concepts, Stream Processing |
| MTML205 | Deep Learning Lab | Lab | 2 | Advanced TensorFlow/PyTorch, Implementing CNNs/RNNs, NLP Project Development, Image Recognition Systems, Hyperparameter Tuning |
| MTEE001 | Elective I (e.g., Reinforcement Learning) | Elective | 3 | Markov Decision Processes, Q-Learning, Policy Gradients, Deep Reinforcement Learning, Exploration-Exploitation Trade-off |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTML301 | Advanced AI Architectures | Core | 4 | Multi-Agent Systems, Knowledge Representation, Explainable AI (XAI), Cognitive Architectures, Neuromorphic Computing |
| MTML302 | AI Ethics and Governance | Core | 3 | Bias and Fairness in AI, Data Privacy and Security, AI Regulations and Policies, Accountability and Transparency, Societal Impact of AI |
| MTEE002 | Elective II (e.g., Cloud Computing for AI) | Elective | 3 | Cloud Platforms for ML, MLOps Practices, Containerization (Docker, Kubernetes), Serverless AI, Distributed Training |
| MTML303 | Seminar | Project | 2 | Technical Literature Review, Research Proposal Development, Scientific Presentation Skills, Report Writing Guidelines, Peer Feedback Integration |
| MTML304 | Project Work Phase I | Project | 6 | Problem Identification and Scope, System Design and Architecture, Initial Data Collection/Generation, Preliminary Implementation, Mid-Term Progress Review |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MTML401 | Project Work Phase II (Dissertation) | Project | 20 | Advanced Model Development, Extensive Experimental Evaluation, Performance Optimization, Comprehensive Thesis Writing, Oral Defense and Presentation |
