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B-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 focuses on developing expertise in intelligent systems and data-driven decision making. It integrates core computer science principles with advanced AI/ML concepts, preparing students for the rapidly evolving Indian tech industry. The program emphasizes practical application and theoretical foundations for cutting-edge technologies.
Who Should Apply?
This program is ideal for ambitious fresh graduates seeking entry into the high-demand fields of AI and ML. It also suits working professionals aiming to upskill for advanced roles, and career changers transitioning into tech from quantitative backgrounds. Strong analytical and problem-solving skills, coupled with a keen interest in data science, are beneficial prerequisites for success.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Scientist, Data Scientist, and AI Consultant. Entry-level salaries range from INR 6-12 LPA, with experienced professionals potentially earning INR 20-50+ LPA. Growth trajectories are steep in Indian tech giants, startups, and research institutions, often aligning with international AI certifications.
Student Success Practices
Foundation Stage
Master Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant time to solidify C/C++ (Semester 1) and Python (Semester 3) programming skills, alongside core engineering mathematics. Regularly solve problems on platforms like HackerRank or CodeChef to build logical thinking and algorithm implementation proficiency.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Khan Academy for Math
Career Connection
A strong foundation in programming and mathematics is critical for entry-level roles in AI/ML, as most core algorithms and data structures rely on these principles. It is directly tested in technical interviews for Indian IT companies.
Active Participation in Labs and Projects- (Semester 1-2)
Engage deeply in all practical lab sessions, understanding the ''''why'''' behind each experiment. Proactively seek opportunities for mini-projects, even beyond curriculum requirements, to apply theoretical knowledge and develop problem-solving skills collaboratively.
Tools & Resources
GitHub for version control, VS Code, Jupyter Notebooks
Career Connection
Practical experience gained in labs and projects is invaluable for building a portfolio. It demonstrates hands-on capability, which is highly valued by Indian employers looking for job-ready graduates in the fast-paced tech sector.
Join Technical Clubs and Peer Learning Groups- (Semester 1-2)
Become an active member of the college''''s AI/ML or Computer Science clubs. Form study groups to discuss complex topics, prepare for exams, and jointly explore new technologies. This fosters a collaborative learning environment.
Tools & Resources
Discord/WhatsApp groups, College Technical Clubs (e.g., Google Developer Students Club)
Career Connection
Networking within the college and learning from peers enhances soft skills like teamwork and communication. These are essential for corporate environments and often play a role in securing internship and full-time positions in India''''s competitive landscape.
Intermediate Stage
Build a Machine Learning Portfolio- (Semester 3-5)
As you learn Machine Learning and Deep Learning, work on personal projects or Kaggle competitions. Focus on understanding real-world datasets, model building, and evaluation. Document your work on GitHub with clear explanations.
Tools & Resources
Kaggle, Google Colab, Scikit-learn, TensorFlow/PyTorch
Career Connection
A robust ML portfolio is crucial for demonstrating your practical skills to recruiters. It showcases your ability to apply theoretical concepts to solve problems, significantly boosting your chances for AI/ML internships and jobs in India.
Seek Early Industry Exposure via Internships- (Semester 4-5)
Actively search for summer internships after your 4th or 5th semester, even if unpaid or in startups. This provides invaluable exposure to industry practices, company culture, and helps in building a professional network within the Indian tech ecosystem.
Tools & Resources
LinkedIn Jobs, Internshala, College Placement Cell
Career Connection
Early internships are often a direct pipeline to pre-placement offers or full-time roles. They provide practical experience highly valued by companies and help you understand specific industry demands in India.
Participate in Hackathons & Coding Challenges- (Semester 3-5)
Regularly participate in hackathons and coding challenges organized by colleges, companies, or platforms. These events hone your rapid problem-solving skills, teamwork, and ability to work under pressure, which are key in fast-paced tech environments.
Tools & Resources
Devpost, Major League Hacking (MLH) events, HackerEarth
Career Connection
Success in hackathons often catches the eye of recruiters and can lead to direct interview opportunities. It also builds confidence and demonstrates your proactive approach, critical for careers in innovative Indian startups and MNCs.
Advanced Stage
Specialize and Undertake Capstone Projects- (Semester 6-8)
Choose professional electives wisely to specialize in an area of AI/ML (e.g., NLP, Computer Vision). Work on a significant capstone project (Internship/Project II, Project Work/Dissertation) that solves a complex real-world problem, ideally with industry mentorship.
Tools & Resources
Research papers (arXiv), Specialized libraries (OpenCV, NLTK), Cloud platforms (AWS, Azure, GCP)
Career Connection
Specialized projects demonstrate deep expertise and readiness for specific AI/ML roles. This is crucial for securing high-paying positions in Indian product companies or research divisions that seek niche skills.
Network Extensively and Attend Conferences- (Semester 6-8)
Attend industry conferences, webinars, and meetups in India (e.g., Data Science Congress, AI Summit). Network with professionals, alumni, and potential employers. Build your LinkedIn profile and actively engage in industry discussions.
Tools & Resources
LinkedIn, Meetup.com, Indian AI/ML Conference websites
Career Connection
Networking is paramount for job hunting, mentorship, and staying updated with industry trends. Many high-value opportunities in India are discovered through professional connections rather than generic job boards.
Master Interview Preparation & Communication- (Semester 7-8)
Practice coding interviews (Data Structures & Algorithms, System Design) rigorously. Work on your communication skills, both technical and non-technical, through mock interviews and presentations. Prepare for behavioral questions and salary negotiations specific to the Indian market.
Tools & Resources
LeetCode, InterviewBit, Glassdoor for company-specific questions, Mock interview platforms
Career Connection
Excellent interview skills are the final hurdle to securing placements. Strong communication helps articulate your technical expertise and career aspirations clearly, making you a more desirable candidate for top companies in India.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Mathematics, and Chemistry/Biology/Biotechnology/Technical Vocational subject with minimum 45% marks (40% for SC/ST category) and appearance in SIT Engineering Entrance Exam (SITEEE).
Duration: 8 semesters / 4 years
Credits: 154 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010101 | Engineering Mathematics – I | Core | 4 | Calculus of one variable, Vector Calculus, Ordinary Differential Equations, Partial Differential Equations, Fourier Series |
| BTE010102 | Engineering Physics | Core | 3 | Quantum Mechanics, Wave Optics, Solid State Physics, Lasers and Fiber Optics, Engineering Materials |
| BTE010103 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion and its control, Fuel Chemistry, Spectroscopic Techniques |
| BTE010104 | Programming for Problem Solving | Core | 3 | Introduction to Programming, Control Statements, Functions and Recursion, Arrays and Strings, Pointers and Structures |
| BTE010105 | English for Communication | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Presentation Skills, Group Discussion Techniques |
| BTE010106 | Engineering Graphics & Design | Core | 1 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to CAD Tools |
| BTE010107 | Workshop Manufacturing Practices | Core | 1 | Carpentry Shop, Fitting Shop, Machining Shop, Welding Shop, Sheet Metal Shop |
| BTE010108 | Engineering Physics Lab | Lab | 1 | Optics Experiments, Semiconductor Devices, Magnetic Fields, Ultrasonics, Laser Properties |
| BTE010109 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Conductivity Measurements, Spectrophotometry, Corrosion Rate Determination, Synthesis of Polymers |
| BTE010110 | Programming for Problem Solving Lab | Lab | 1 | C Programming Practice, Conditional Statements and Loops, Function Implementation, Array and String Operations, File Handling and Pointers |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010201 | Engineering Mathematics – II | Core | 4 | Linear Algebra, Laplace Transforms, Complex Analysis, Probability Theory, Statistical Methods |
| BTE010202 | Basic Electrical Engineering | Core | 3 | DC Circuits Analysis, AC Circuits Analysis, Transformers, Electrical Machines, Single Phase Power Systems |
| BTE010203 | Basic Civil and Mechanical Engineering | Core | 3 | Surveying and Leveling, Building Materials, Thermodynamics Principles, Internal Combustion Engines, Refrigeration and Air Conditioning |
| BTE010204 | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Sustainable Development, Environmental Impact Assessment |
| BTE010205 | Data Structures & Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| BTE010206 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, AC Circuit Measurements, Transformer Characteristics, DC Machine Testing, AC Machine Control |
| BTE010207 | Data Structures & Algorithms Lab | Lab | 1 | Array and List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| BTE010208 | Sports (Credit Based) | Core | 1 | Physical Fitness, Team Sports, Individual Sports, Health and Wellness, Sportsmanship |
| BTE010209 | General Elective – I (Human Values) | Elective | 3 | Introduction to Human Values, Ethics and Morality, Universal Human Values, Professional Ethics, Social Responsibility |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010301 | Discrete Mathematics | Core | 3 | Mathematical Logic, Set Theory and Relations, Functions and Sequences, Graph Theory, Combinatorics and Probability |
| BTE010302 | Computer Organization & Architecture | Core | 3 | Digital Logic Circuits, CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining and Parallel Processing |
| BTE010303 | Database Management Systems | Core | 3 | ER Model and Relational Model, SQL and Relational Algebra, Normalization and Dependencies, Transaction Management, Concurrency Control and Recovery |
| BTE010304 | Python Programming | Core | 3 | Python Basics and Data Types, Control Flow and Functions, Object-Oriented Programming in Python, File Handling and Modules, NumPy and Pandas for Data Manipulation |
| BTE010305 | Artificial Intelligence | Core | 3 | Introduction to AI, Heuristic Search Techniques, Knowledge Representation, Logic Programming, Expert Systems and Fuzzy Logic |
| BTE010306 | Database Management Systems Lab | Lab | 1 | SQL Queries Practice, Database Schema Design, Joins and Subqueries, Trigger and Stored Procedures, Mini Project on DBMS |
| BTE010307 | Python Programming Lab | Lab | 1 | Python Scripting, Data Structure Implementation, Object-Oriented Programs, Data Analysis with Libraries, Web Scraping Basics |
| BTE010308 | Artificial Intelligence Lab | Lab | 1 | Search Algorithm Implementation, Logic Programming with Prolog, Knowledge Representation Systems, Expert System Shells, AI Game Playing |
| BTE010309 | Audit Course – I | Audit | 0 | Foreign Language Basics, National Service Scheme (NSS), National Cadet Corps (NCC), Yoga and Meditation, Music/Dance Appreciation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010401 | Design & Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms and NP-Completeness |
| BTE010402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems and I/O |
| BTE010403 | Object Oriented Programming | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, Generics and Collections |
| BTE010404 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning Basics, Model Evaluation and Selection |
| BTE010405 | Web Technology | Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, Client-Server Architecture, Web Servers and Databases, Introduction to Web Frameworks |
| BTE010406 | Operating Systems Lab | Lab | 1 | Process Management Commands, CPU Scheduling Algorithms, Memory Management Simulations, Shell Scripting, File System Operations |
| BTE010407 | Object Oriented Programming Lab | Lab | 1 | Class and Object Implementation, Inheritance and Polymorphism, Abstract Classes and Interfaces, Exception Handling Exercises, File I/O and Collections |
| BTE010408 | Machine Learning Lab | Lab | 1 | Linear Regression Implementation, Classification Algorithms (e.g., SVM, Decision Tree), Clustering Algorithms (e.g., K-Means), Model Training and Evaluation, Feature Engineering |
| BTE010409 | Mini Project | Project | 1 | Problem Identification, Design and Planning, Implementation and Testing, Documentation, Presentation |
| BTE010410 | Audit Course – II | Audit | 0 | Professional Ethics, Business Communication Skills, Innovation and Creativity, Conflict Resolution, Personality Development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010501 | Theory of Computation | Core | 3 | Finite Automata and Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| BTE010502 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP) |
| BTE010503 | Deep Learning | Core | 3 | Neural Network Fundamentals, Perceptrons and Activation Functions, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) |
| BTE010504 | Data Science | Core | 3 | Data Preprocessing and Cleaning, Exploratory Data Analysis (EDA), Data Visualization, Statistical Inference, Predictive Modeling |
| BTE010505 | Professional Elective – I (Natural Language Processing) | Elective | 3 | Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Basics |
| BTE010506 | Deep Learning Lab | Lab | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Transfer Learning, Hyperparameter Tuning |
| BTE010507 | Data Science Lab | Lab | 1 | Data Cleaning with Pandas, Visualization with Matplotlib/Seaborn, Statistical Hypothesis Testing, Building Predictive Models, Feature Selection Techniques |
| BTE010508 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis with Wireshark, Routing Protocols Implementation, Client-Server Communication |
| BTE010509 | Summer Internship – I | Internship | 2 | Industry Exposure, Practical Skill Application, Project Documentation, Professional Networking, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010601 | Software Engineering | Core | 3 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Techniques, Project Management and Maintenance |
| BTE010602 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization and Generation |
| BTE010603 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Data Warehousing and ETL |
| BTE010604 | Professional Elective – II (Data Mining) | Elective | 3 | Introduction to Data Mining, Association Rule Mining, Classification Algorithms, Clustering Techniques, Outlier Detection and Web Mining |
| BTE010605 | Open Elective – I (Digital Marketing) | Elective | 3 | Search Engine Optimization (SEO), Search Engine Marketing (SEM), Social Media Marketing, Content Marketing, Email Marketing and Analytics |
| BTE010606 | Big Data Analytics Lab | Lab | 1 | Hadoop Setup and Operations, MapReduce Programming, Spark Applications, Hive and Pig Scripting, NoSQL Database Interaction |
| BTE010607 | Project Based Learning (PBL) | Project | 2 | Collaborative Problem Solving, Research and Analysis, Prototyping and Development, Teamwork and Communication, Project Presentation |
| BTE010608 | Audit Course – III | Audit | 0 | Research Methodology Basics, Intellectual Property Rights (IPR), Cyber Security Awareness, Disaster Management, Indian Constitution |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010701 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Value and Policy Iteration, Q-learning and SARSA, Deep Reinforcement Learning, Exploration vs Exploitation |
| BTE010702 | Professional Elective – III (Computer Vision) | Elective | 3 | Image Formation and Filtering, Feature Detection and Extraction, Image Segmentation, Object Recognition and Tracking, Deep Learning for Vision |
| BTE010703 | Professional Elective – IV (Quantum Computing) | Elective | 3 | Quantum Bits (Qubits), Superposition and Entanglement, Quantum Gates and Circuits, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography Basics |
| BTE010704 | Open Elective – II (Operations Research) | Elective | 3 | Linear Programming, Simplex Method, Transportation Problem, Assignment Problem, Network Models and Queuing Theory |
| BTE010705 | Internship / Project – II | Internship/Project | 6 | Advanced Project Development, Industry-Specific Problem Solving, Real-world System Implementation, Mentorship and Feedback, Comprehensive Report and Presentation |
| BTE010706 | Research Project (Optional) | Project | 3 | Literature Review, Problem Formulation, Methodology Design, Data Analysis and Interpretation, Thesis Writing and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTE010801 | Professional Elective – V (Ethical AI) | Elective | 3 | AI Ethics Principles, Bias and Fairness in AI, Accountability and Transparency, AI and Privacy Concerns, AI Regulations and Governance |
| BTE010802 | Project Work / Dissertation | Project | 12 | Independent Research and Development, Large-Scale System Design, Advanced Algorithmic Implementation, Comprehensive Testing and Validation, Final Thesis and Viva-Voce |
| BTE010803 | Advanced Professional Skills | Core | 2 | Advanced Communication, Teamwork and Collaboration, Leadership and Mentoring, Critical Thinking and Problem Solving, Professional Etiquette and Ethics |
