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B-SC-ARTIFICIAL-INTELLIGENCE-HONOURS-WITH-RESEARCH in General at Symbiosis International University
Pune, Maharashtra
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About the Specialization
What is General at Symbiosis International University Pune?
This B.Sc. (Artificial Intelligence) Honours with Research program at Symbiosis International University focuses on providing a deep, research-oriented understanding of AI. It delves into advanced concepts of machine learning, deep learning, natural language processing, and robotics, catering to the burgeoning demand for AI specialists across Indian industries. The program''''s interdisciplinary approach prepares students for complex problem-solving using cutting-edge AI technologies, setting them apart in a competitive landscape.
Who Should Apply?
This program is ideal for analytically-minded fresh graduates with a strong foundation in mathematics and programming, aspiring to build a career in advanced AI research or development. It also suits working professionals seeking to transition into AI-focused roles or upskill with the latest AI advancements. Individuals keen on academic research, innovation, and contributing to the AI knowledge base within India will find this curriculum highly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue advanced roles as AI Scientists, Machine Learning Engineers, Research Analysts, or Data Scientists in leading Indian technology firms, research labs, and startups. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The Honours with Research component fosters strong analytical and problem-solving skills, aligning with the growing need for specialized AI talent capable of innovation and R&D in India''''s tech ecosystem.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate consistent time to practice core programming concepts in C, C++, and Python, focusing on data structures and algorithms. Utilize online coding platforms to solve problems regularly and participate in introductory coding challenges to solidify foundational skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, freeCodeCamp
Career Connection
Strong programming skills are foundational for any AI role, enhancing problem-solving abilities critical for technical interviews and project development in the Indian tech industry.
Build a Strong Mathematical Base- (Semester 1-2)
Focus on understanding the theoretical underpinnings of discrete mathematics, linear algebra, and statistics. Attend extra tutorials, engage in peer study groups, and solve a wide range of problems to solidify conceptual clarity, which is vital for advanced AI topics.
Tools & Resources
Khan Academy, NPTEL courses, MIT OpenCourseware, specialized textbooks
Career Connection
A robust mathematical understanding is crucial for comprehending AI algorithms, developing new models, and excelling in quantitative roles, particularly in R&D departments.
Engage in Early Project Exploration- (Semester 1-2)
Start working on small, self-initiated projects using basic programming knowledge. Collaborate with peers on simple applications or explore open-source contributions. Document your learning and project outcomes to build a preliminary portfolio.
Tools & Resources
GitHub, online tutorials, college hackathons, Stack Overflow
Career Connection
Early project experience builds a portfolio, fosters practical problem-solving, and demonstrates initiative, making students more attractive for internships and entry-level positions in Indian startups.
Intermediate Stage
Apply Machine Learning Practically- (Semester 3-5)
Actively implement machine learning algorithms using Python libraries like Scikit-learn, Pandas, and NumPy. Participate in Kaggle competitions or similar data science challenges to gain hands-on experience with real-world datasets and evaluate model performance.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, Coursera/edX ML courses
Career Connection
Practical ML application skills are directly sought after for Machine Learning Engineer and Data Scientist roles, significantly improving chances for relevant internships and jobs in India.
Network with Industry Professionals- (Semester 3-5)
Attend AI/tech meetups, workshops, and industry conferences (both online and offline) to connect with professionals. Utilize platforms like LinkedIn to build a professional network and seek mentorship or insights into industry trends and career paths in India.
Tools & Resources
LinkedIn, Meetup.com, Nasscom events, university alumni network
Career Connection
Networking opens doors to internship opportunities, industry insights, and potential job referrals within the Indian tech ecosystem, crucial for career advancement.
Contribute to Open-Source AI Projects- (Semester 4-5)
Identify and contribute to open-source AI projects on GitHub. This could involve bug fixes, feature enhancements, documentation, or testing. It demonstrates collaborative skills and proficiency in real-world AI development workflows.
Tools & Resources
GitHub, various open-source AI repositories (e.g., Hugging Face, TensorFlow, PyTorch)
Career Connection
Open-source contributions serve as a powerful portfolio addition, showcasing practical skills and dedication, highly valued by Indian tech companies seeking proactive candidates.
Advanced Stage
Undertake Comprehensive Research Projects- (Semester 7-8)
Engage deeply in the Honours with Research project, focusing on a novel problem statement. Publish findings in reputable conferences or journals, collaborating with faculty advisors and potentially industry mentors to gain research exposure.
Tools & Resources
Research paper databases (e.g., IEEE Xplore, arXiv), academic writing tools, institutional research labs
Career Connection
High-quality research work and publications are critical for pursuing higher education (Masters/Ph.D.) or R&D roles in leading Indian AI labs and global tech companies.
Specialize and Develop Niche Skills- (Semester 6-8)
Leverage elective choices to specialize in a particular AI sub-field like NLP, Computer Vision, or Reinforcement Learning. Pursue advanced certifications or online specializations to deepen expertise in your chosen area, enhancing marketability.
Tools & Resources
NVIDIA Deep Learning Institute, Google AI Certifications, specialized MOOCs (e.g., from Stanford, deeplearning.ai)
Career Connection
Niche expertise makes graduates highly valuable for specialized AI roles in Indian companies focusing on particular domains or advanced technologies, commanding better compensation.
Prepare for Advanced Placements & Interviews- (Semester 6-8)
Systematically prepare for technical interviews, focusing on advanced AI concepts, system design, and behavioral questions. Practice mock interviews with peers and career counselors, and refine your resume and research portfolio for top-tier AI firms and research institutions.
Tools & Resources
InterviewBit, LeetCode (Hard problems), Glassdoor, university career services, alumni network mentors
Career Connection
Thorough preparation is key to securing competitive placements as AI Scientists, ML Engineers, or AI Researchers in India''''s leading tech and research organizations, ensuring career success.
Program Structure and Curriculum
Eligibility:
- Passed Standard XII (10+2) or equivalent examination from any recognized Board with Mathematics as one of the subjects and obtained minimum 50% marks (45% for Scheduled Caste/Scheduled Tribes) at qualifying examination. Mandatory to appear for Symbiosis Entrance Test (SET).
Duration: 4 years / 8 semesters
Credits: 178 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6930 | Foundation of Mathematics | Core | 4 | Sets, Relations and Functions, Logic, Matrices and Determinants, Combinatorics and Probability, Mathematical Reasoning |
| T6931 | Fundamentals of Computer | Core | 4 | Computer Fundamentals, Operating Systems Basics, Number Systems and Codes, Data Representation, Computer Networks Overview |
| T6932 | Programming in C | Core | 4 | Introduction to C, Data Types and Operators, Control Structures, Functions and Pointers, Arrays and Strings, File Handling |
| T6933 | Programming in C Lab | Lab | 2 | Implementation of C programs for control structures, Functions and recursion, Arrays and multi-dimensional arrays, Pointers and memory management, String manipulation |
| T6934 | Professional Communication | Core | 4 | Communication Process and Barriers, Non-Verbal Communication, Formal Writing Skills, Presentation Techniques, Group Discussions and Interviews |
| T6935 | Digital Electronics | Core | 4 | Number Systems, Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6936 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Trees, Lattices and Boolean Algebra |
| T6937 | Data Structures | Core | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Binary Trees, Graphs and Graph Algorithms |
| T6938 | Object Oriented Programming with C++ | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Constructors and Destructors, Exception Handling and File I/O |
| T6939 | Data Structures Lab | Lab | 2 | Implementation of arrays and linked lists, Stack and queue operations, Tree traversals, Graph algorithms, Sorting and searching algorithms |
| T6940 | Web Development using HTML & CSS | Core | 4 | HTML Structure and Elements, CSS Styling and Selectors, Layout and Responsive Design, Introduction to JavaScript, Web Forms and Multimedia |
| T6941 | Principles of Management | Core | 4 | Introduction to Management, Planning and Decision Making, Organizing and Staffing, Directing and Motivation, Controlling and Ethics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6942 | Statistical Methods for AI | Core | 4 | Probability and Random Variables, Probability Distributions, Sampling and Estimation, Hypothesis Testing, Regression and Correlation |
| T6943 | Database Management Systems | Core | 4 | DBMS Concepts and Architecture, Relational Model and Algebra, SQL Queries and Operations, Normalization, Transaction Management and Concurrency Control |
| T6944 | Python Programming | Core | 4 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, File Handling and Exception Handling |
| T6945 | Database Management Systems Lab | Lab | 2 | DDL and DML commands, SQL queries for data retrieval and manipulation, Normalization implementation, Stored procedures and functions, Database connectivity |
| T6946 | Python Programming Lab | Lab | 2 | Practical Python programming exercises, Working with lists, tuples, dictionaries, Creating and using functions, Object-oriented programming examples, File I/O operations |
| T6947 | Operating Systems Concepts | Core | 4 | OS Introduction and Types, Process Management and CPU Scheduling, Memory Management, Virtual Memory, File Systems and I/O Management |
| T6948 | Introduction to Artificial Intelligence | Core | 4 | What is AI, History of AI, Problem Solving Agents, Search Algorithms (uninformed, informed), Game Playing and Adversarial Search, Knowledge Representation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6949 | Linear Algebra for AI | Core | 4 | Vectors and Matrices, Vector Spaces and Subspaces, Eigenvalues and Eigenvectors, Linear Transformations, Matrix Decomposition (SVD, PCA) |
| T6950 | Machine Learning | Core | 4 | ML Introduction and Types, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods and Boosting |
| T6951 | Java Programming | Core | 4 | Java Fundamentals and Syntax, Object-Oriented Programming in Java, Exception Handling, Multithreading, GUI Programming (AWT/Swing) |
| T6952 | Machine Learning Lab | Lab | 2 | Implementation of linear and logistic regression, Decision tree and SVM implementation, Clustering algorithms (K-Means), Model evaluation metrics, Using Scikit-learn and Pandas |
| T6953 | Java Programming Lab | Lab | 2 | Java programming exercises, OOP concepts implementation, Exception handling in Java, Multithreaded applications, Basic GUI application development |
| T6954 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical Layer and Data Link Layer, Network Layer and IP Addressing, Transport Layer (TCP, UDP), Application Layer Protocols |
| T6955 | Project I (Mini Project) | Project | 2 | Problem identification and scope definition, Requirement analysis, Design and architecture, Implementation and testing, Project documentation and presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6956 | Deep Learning | Core | 4 | Introduction to Deep Learning, Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs |
| T6957 | Natural Language Processing | Core | 4 | NLP Introduction and Challenges, Text Preprocessing and Tokenization, Language Models (N-grams, Word Embeddings), Text Classification, Information Extraction and Question Answering |
| T6958 | Deep Learning Lab | Lab | 2 | Implementing neural networks with TensorFlow/Keras, Building and training CNNs, Developing RNNs for sequence data, Hyperparameter tuning, Model deployment concepts |
| T6959 | Elective I | Elective | 4 | Key topics depend on chosen elective from the list provided by the institution (e.g., Computer Vision, Robotics, Big Data Analytics) |
| T6963 | Research Methodology | Core | 4 | Introduction to Research, Research Design and Problem Formulation, Data Collection Methods, Data Analysis and Interpretation, Research Report Writing and Ethics |
| T6964 | AI in Robotics | Core | 4 | Robotics Fundamentals, Robot Kinematics and Dynamics, Sensing and Perception in Robotics, Robot Control Architectures, Path Planning and Navigation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T6965 | Reinforcement Learning | Core | 4 | RL Introduction and Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| T6966 | Ethics in AI | Core | 4 | Ethical Principles for AI, Bias and Fairness in AI, Privacy and Data Protection, Accountability and Transparency, Societal Impact of AI |
| T6967 | Elective II | Elective | 4 | Key topics depend on chosen elective from the list provided by the institution (e.g., Advanced Machine Learning, Explainable AI, Cloud Computing) |
| T6971 | Elective III | Elective | 4 | Key topics depend on chosen elective from the list provided by the institution (e.g., Image and Video Processing, Cyber Security and AI, IoT and Edge AI) |
| T6975 | Project II | Project | 6 | Advanced project development, System design and architecture, Complex implementation and testing, Deployment and evaluation, Final project presentation and report |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T7021 | Advanced Data Science | Core (Honours) | 4 | Big Data Ecosystems and Technologies, Advanced Statistical Modeling, Time Series Analysis, Feature Engineering and Selection, Interactive Data Visualization |
| T7022 | Specialization Elective I (Honours) | Elective (Honours) | 4 | Key topics depend on chosen elective from the list provided by the institution (e.g., Quantum Computing, Federated Learning, Advanced NLP) |
| T7026 | Research Seminar | Research | 4 | Literature Review and Gap Identification, Formulating Research Questions, Developing a Research Proposal, Presentation Skills for Research, Academic Writing and Peer Review Process |
| T7027 | Dissertation/Research Project I | Research | 8 | Defining a research problem, Conducting comprehensive literature survey, Designing research methodology, Developing preliminary prototypes/experiments, Initial data collection and analysis |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| T7028 | Advanced AI Architectures | Core (Honours) | 4 | Generative Adversarial Networks (GANs), Transformer Networks, Graph Neural Networks (GNNs), Multi-modal AI, Efficient AI and Edge AI Architectures |
| T7029 | Specialization Elective II (Honours) | Elective (Honours) | 4 | Key topics depend on chosen elective from the list provided by the institution (e.g., Neuro-Symbolic AI, Explainable AI in Practice, AI for Cybersecurity) |
| T7033 | Dissertation/Research Project II | Research | 12 | Conducting extensive experimentation, Advanced data analysis and interpretation, Scientific writing and thesis preparation, Presenting research findings, Defending the dissertation |
