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M-TECH-MASTER-OF-TECHNOLOGY-SIT-PUNE in Artificial Intelligence And Machine Learning at Symbiosis International University (SIU)
Pune, Maharashtra
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About the Specialization
What is Artificial Intelligence and Machine Learning at Symbiosis International University (SIU) Pune?
This M.Tech (Artificial Intelligence and Machine Learning) program at Symbiosis International University, Pune, focuses on equipping students with advanced knowledge and practical skills in cutting-edge AI and ML technologies. In the rapidly evolving Indian tech landscape, this specialization is crucial for developing intelligent systems, driving innovation in sectors like finance, healthcare, and e-commerce. The program''''s blend of theoretical foundations and hands-on application distinguishes it for future AI leaders.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech in Computer Science, IT, E&TC) and postgraduates (MCA, M.Sc. CS/IT) holding a keen interest in data-driven problem-solving and algorithmic thinking. It caters to fresh graduates seeking entry into the high-demand AI/ML field in India, as well as working professionals looking to upskill or transition into advanced roles like AI Engineer, Data Scientist, or Machine Learning Architect.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as AI Engineers, Machine Learning Scientists, Data Scientists, or AI/ML Consultants. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals commanding significantly higher packages. The program fosters critical thinking and problem-solving abilities, aligning with industry demand for professionals who can design, develop, and deploy complex AI solutions in Indian and global companies.
Student Success Practices
Foundation Stage
Master Mathematical & Algorithmic Foundations- (Semester 1)
Dedicate significant time to understanding the core mathematics (Linear Algebra, Calculus, Probability, Statistics) and advanced data structures/algorithms. Utilize online platforms for problem-solving and competitive programming. This forms the bedrock for advanced AI/ML concepts.
Tools & Resources
Khan Academy, NPTEL courses, HackerRank, LeetCode, GeeksforGeeks, Python/NumPy for implementation
Career Connection
Strong fundamentals are critical for acing technical interviews, designing efficient AI models, and understanding research papers, directly impacting placement in top tech firms.
Build Foundational AI/ML Projects- (Semester 1)
Start working on small, end-to-end projects immediately, even if they are based on simple datasets (e.g., Iris, MNIST). Focus on implementing algorithms taught in class from scratch or using basic libraries. Participate in college-level hackathons.
Tools & Resources
Kaggle datasets, scikit-learn, TensorFlow/PyTorch basics, Jupyter Notebooks, GitHub for version control
Career Connection
Practical project experience demonstrates problem-solving ability and hands-on skills to recruiters, making your resume stand out for internships and entry-level positions.
Engage in Peer Learning & Study Groups- (Semester 1)
Form study groups with classmates to discuss complex topics, solve problems together, and explain concepts to each other. This reinforces understanding, builds a support network, and improves communication skills essential for team-based industry projects.
Tools & Resources
Google Meet, WhatsApp groups, Shared whiteboards, University library resources
Career Connection
Collaboration skills are highly valued in the tech industry. Effective teamwork in academic settings translates to better performance in group projects during internships and professional roles.
Intermediate Stage
Deep Dive into Specialized Areas & Research- (Semester 2)
Identify areas of interest (e.g., NLP, Computer Vision, MLOps) and pursue advanced learning beyond coursework through online specializations, advanced books, or research papers. Seek opportunities to assist faculty with their research or undertake mini-research projects.
Tools & Resources
Coursera/edX specializations (DeepLearning.AI), ArXiv, Google Scholar, University research labs
Career Connection
Specialization and research exposure enhance your profile for niche roles, R&D positions, and potentially PhD opportunities. It also provides deeper understanding for complex interview questions.
Secure & Excel in an Industry Internship- (Semester 2)
Actively seek and apply for internships at AI/ML companies, startups, or R&D divisions, preferably in your area of specialization. Treat the internship as an extended interview, demonstrating strong work ethic, technical skills, and problem-solving aptitude.
Tools & Resources
LinkedIn, Internshala, Company career pages, University placement cell, Industry mentors
Career Connection
Internships are the most direct path to pre-placement offers (PPOs) and provide invaluable real-world experience, making you highly employable upon graduation.
Build a Robust Portfolio with Capstone Projects- (Semester 2)
Design and execute substantial, industry-relevant projects, ideally solving a real-world problem or contributing to open source. Focus on end-to-end development, including data collection, model building, deployment, and evaluation. Showcase these on GitHub and personal websites.
Tools & Resources
FastAPI/Flask for deployment, AWS/GCP/Azure free tiers, Docker, Streamlit for demos, GitHub Pages
Career Connection
A strong project portfolio is crucial for showcasing advanced skills and practical application, often being the deciding factor in securing top placements and attracting attention from hiring managers.
Advanced Stage
Drive a High-Impact Dissertation Project- (Semester 3-4)
Treat your Dissertation Phase I and II as a real-world R&D project. Identify a novel problem, conduct thorough literature reviews, implement robust solutions, and meticulously analyze results. Aim for a publication in a conference or journal.
Tools & Resources
Research papers (ArXiv, IEEE Xplore, ACM Digital Library), Specialized software (MATLAB, powerful GPUs), LaTeX for thesis writing, Academic guidance
Career Connection
A strong dissertation showcases your ability to conduct independent research, innovate, and contribute to the field, making you highly attractive for R&D roles, academic positions, or advanced engineering roles.
Master Industry-Standard Deployment & MLOps- (Semester 3-4)
Beyond model building, focus on the entire lifecycle of an AI product. Learn to deploy models using cloud platforms (AWS, Azure, GCP), understand CI/CD pipelines for ML, and implement monitoring tools. Gain proficiency in MLOps practices.
Tools & Resources
Docker, Kubernetes, AWS SageMaker, Azure ML, Google Cloud AI Platform, MLflow, Airflow
Career Connection
Expertise in MLOps is a highly sought-after skill, positioning you for roles like ML Engineer, MLOps Engineer, or AI Solution Architect, ensuring your models move from prototype to production.
Network Strategically & Prepare for Placements- (Semester 3-4)
Actively participate in industry seminars, workshops, and AI/ML conferences. Connect with alumni and industry professionals on LinkedIn. Refine your resume, cover letter, and interview skills, focusing on behavioral and technical questions relevant to AI/ML roles.
Tools & Resources
LinkedIn, Industry meetups (e.g., through Meetup.com), Professional bodies (IEEE, ACM), Mock interviews, Online interview prep platforms
Career Connection
A strong professional network can open doors to opportunities. Thorough placement preparation ensures you convert opportunities into successful career starts in leading Indian and global tech companies.
Program Structure and Curriculum
Eligibility:
- B.E./ B.Tech in Computer Engineering/ Information Technology/ Electronics/ Electronics & Telecommunication/ Instrumentation, or MCA/ M.Sc. (Computer Science/Information Technology) with minimum 50% marks (45% for SC/ST category) or equivalent grade.
Duration: 2 years / 4 semesters
Credits: 80 Credits
Assessment: Internal: 40% (for theory courses), 60% (for practical/lab courses and dissertation), External: 60% (for theory courses), 40% (for practical/lab courses and dissertation viva)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 790101 | Mathematical Foundation for AI & ML | Core | 4 | Linear Algebra, Calculus and Optimization, Probability and Statistics, Discrete Mathematics, Random Variables and Distributions |
| 790102 | Advanced Data Structures & Algorithms | Core | 4 | Asymptotic Analysis, Advanced Tree Structures (AVL, B-Trees), Graph Algorithms, Dynamic Programming, Greedy Algorithms, Hashing Techniques |
| 790103 | Foundations of Artificial Intelligence | Core | 4 | Introduction to AI, Problem Solving Agents, Knowledge Representation, Logical Reasoning, Heuristic Search Techniques, Expert Systems |
| 790104 | Statistical Foundation for Machine Learning | Core | 4 | Probability Theory, Hypothesis Testing, Regression Analysis, Classification Models, Bayesian Statistics, Dimensionality Reduction |
| 790105 | Mathematical Foundation for AI & ML Lab | Lab | 2 | Implementation of Linear Algebra concepts, Statistical calculations using Python, Optimization problem solving, Probability distribution simulations, Numerical methods for AI/ML |
| 790106 | Advanced Data Structures & Algorithms Lab | Lab | 2 | Implementation of Trees and Graphs, Algorithm design and analysis, Sorting and Searching algorithms, Dynamic Programming solutions, Performance evaluation of algorithms |
| 790107 | Research Methodology & IPR | Core (Research) | 2 | Research Problem Formulation, Literature Review Techniques, Research Design and Methods, Data Collection and Analysis, Report Writing and Presentation, Intellectual Property Rights and Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 790108 | Advanced Machine Learning | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Techniques, Reinforcement Learning Fundamentals, Ensemble Methods (Bagging, Boosting), Kernel Methods and SVMs, Model Evaluation and Selection |
| 790109 | Deep Learning | Core | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers and Attention Mechanisms, Generative Adversarial Networks (GANs), Optimization and Regularization in Deep Learning |
| 790110 | Natural Language Processing | Core | 4 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Language Models (N-gram, BERT), Sequence Labeling (POS tagging, NER), Sentiment Analysis, Machine Translation |
| 790111 | Machine Learning Operations (MLOps) | Core | 4 | ML Lifecycle Management, Data Versioning and Governance, Model Deployment Strategies, Monitoring and Retraining, CI/CD for Machine Learning, Reproducibility and Scalability |
| 790112 | Advanced Machine Learning Lab | Lab | 2 | Implementation of various ML algorithms, Model training, validation, and testing, Hyperparameter tuning, Feature engineering techniques, Case studies and real-world applications |
| 790113 | Deep Learning Lab | Lab | 2 | Building and training CNNs and RNNs, Image classification and object detection, Natural Language Understanding tasks, TensorFlow and PyTorch frameworks, Deep learning model optimization |
| 790114 | Technical Paper Writing & Seminar | Project/Seminar | 2 | Structure of a Technical Paper, Academic Writing Skills, Literature Review and Referencing, Data Presentation and Visualization, Public Speaking and Presentation Skills, Peer Review and Feedback |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 790115 | Data Science with Big Data | Core | 4 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for Data Processing, Data Warehousing and Data Lakes, NoSQL Databases, Big Data Analytics Techniques |
| 790116 | Explainable AI | Core | 4 | Interpretability vs. Explainability, Post-hoc Explanations (LIME, SHAP), Model-agnostic Explanations, Fairness and Bias in AI, Causal Inference in ML, Ethical Considerations in AI |
| 790117 | Elective-I (Example: Computer Vision) | Elective | 4 | Image Processing Fundamentals, Feature Extraction and Matching, Object Detection and Recognition, Image Segmentation, Deep Learning for Vision, Applications of Computer Vision |
| 790118 | Elective-II (Example: Advanced Robotics) | Elective | 4 | Robot Kinematics and Dynamics, Path Planning Algorithms, Robot Control Architectures, Robot Vision and Sensing, Machine Learning in Robotics, Human-Robot Interaction |
| 790119 | Dissertation Phase-I | Project | 6 | Problem Identification and Scope Definition, Comprehensive Literature Survey, Research Gap Analysis, Proposal Writing and Presentation, Methodology Design and Planning, Initial Data Collection and Setup |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 790120 | Elective-III (Example: Cloud Computing) | Elective | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technologies, Cloud Security and Privacy, Cloud Platforms (AWS, Azure, GCP), Serverless Computing |
| 790121 | Elective-IV (Example: Blockchain Technology) | Elective | 4 | Cryptographic Primitives, Distributed Ledger Technology, Smart Contracts and DApps, Consensus Mechanisms, Blockchain Platforms (Ethereum, Hyperledger), Blockchain Applications in AI |
| 790122 | Dissertation Phase-II | Project | 10 | System Design and Implementation, Experimental Setup and Execution, Result Analysis and Interpretation, Thesis Writing and Documentation, Plagiarism Check and Ethical Submission, Viva Voce Preparation and Defense |
