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B-SC-ARTIFICIAL-INTELLIGENCE in General at Saveetha Institute of Medical and Technical Sciences
Chennai, Tamil Nadu
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About the Specialization
What is General at Saveetha Institute of Medical and Technical Sciences Chennai?
This B.Sc. Artificial Intelligence program at Saveetha Institute of Medical and Technical Sciences focuses on equipping students with foundational and advanced knowledge in AI. It emphasizes core concepts like machine learning, deep learning, natural language processing, and big data analytics, addressing the growing demand for AI professionals in India''''s rapidly expanding tech sector. The curriculum is designed to foster innovation and practical problem-solving skills, making graduates highly competitive in the Indian job market.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, programming, and logical reasoning, seeking entry into the AI and data science fields. It also benefits individuals passionate about developing intelligent systems and solving complex real-world problems. Specific prerequisite backgrounds typically include 10+2 with a strong focus on Mathematics and Computer Science, preparing them for the rigorous technical demands of the curriculum.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Developer, Data Scientist, NLP Specialist, and Robotics Engineer. Entry-level salaries in India for AI roles typically range from INR 4-8 LPA, with experienced professionals earning upwards of INR 15-30 LPA. The program aligns with industry needs, paving the way for advanced studies or roles in leading Indian tech companies and startups.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate consistent time to practice Python and C++ programming, focusing on data structures and algorithms. Build small projects to apply concepts learned in labs and theory classes.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong programming skills are non-negotiable for AI roles and are heavily assessed in technical interviews for placements.
Build a Strong Mathematical Core- (Semester 1-2)
Actively engage with Calculus, Linear Algebra, Probability, and Statistics. Utilize online resources and practice problems to solidify understanding, as these form the bedrock of AI and Machine Learning.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Linear Algebra), NCERT/Standard Textbooks
Career Connection
A robust mathematical foundation is crucial for understanding complex AI algorithms and for research-oriented roles.
Engage in Peer Learning Groups- (Semester 1-2)
Form study groups with peers to discuss difficult concepts, solve problems collaboratively, and prepare for exams. Teaching others reinforces your own understanding.
Tools & Resources
Discord, Google Meet, University Library Study Rooms
Career Connection
Develops teamwork and communication skills, essential for collaborative projects in the industry.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Start applying theoretical knowledge of AI, Machine Learning, and Databases to real-world datasets. Participate in Kaggle competitions or build personal projects to enhance your portfolio.
Tools & Resources
Kaggle, GitHub, Google Colab, Scikit-learn
Career Connection
Practical experience through projects demonstrates problem-solving abilities and makes your resume stand out for internships and job applications.
Network with Industry Professionals- (Semester 3-5)
Attend workshops, webinars, and conferences organized by the department or local tech communities. Connect with AI professionals on LinkedIn to gain insights into industry trends and career paths.
Tools & Resources
LinkedIn, Meetup.com, Industry specific events
Career Connection
Opens doors to internship opportunities, mentorship, and a better understanding of industry expectations, aiding in strategic career planning.
Specialize in a Niche AI Area- (Semester 4-5)
Explore elective subjects like Deep Learning, NLP, or Computer Vision more deeply. Take online courses or certifications in your chosen area to build specialized expertise beyond the curriculum.
Tools & Resources
Coursera (DeepLearning.AI), Udemy, NVIDIA Deep Learning Institute
Career Connection
Developing specialized skills makes you a more attractive candidate for specific roles in high-demand AI domains.
Advanced Stage
Focus on Capstone Project Excellence- (Semester 5-6)
Invest significant effort in your final year project, aiming for an innovative solution with a strong practical impact. Document your work meticulously and prepare for robust presentations.
Tools & Resources
GitHub for version control, LaTeX for documentation, Project Management tools
Career Connection
A strong capstone project is a key talking point in interviews, demonstrating your ability to execute a complete AI solution.
Prepare Rigorously for Placements and Internships- (Semester 5-6)
Practice aptitude, technical, and HR interview questions. Work on improving soft skills, communication, and resume building. Actively seek out and apply for internships and full-time positions.
Tools & Resources
Placement cell resources, Mock interview platforms, Job portals like Naukri, LinkedIn Jobs
Career Connection
Directly enhances employability and secures rewarding opportunities upon graduation in the competitive Indian job market.
Cultivate Ethical AI Awareness- (Semester 6)
Beyond technical skills, understand the ethical implications, biases, and societal impact of AI technologies. Integrate ethical considerations into project development and discussions.
Tools & Resources
Online articles on AI Ethics, Discussions with faculty and peers, Relevant MOOCs
Career Connection
Companies increasingly value professionals who can develop AI responsibly and ethically, a critical skill for future leadership roles in AI.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 3 years / 6 semesters
Credits: 120 Credits
Assessment: Internal: Continuous Assessment Marks (weightage varies by course), External: End Semester Exam Marks (weightage varies by course)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA101 | Calculus and Linear Algebra | Core | 4 | Matrices and Eigenvalues, Vector Spaces, Calculus of One Variable, Multivariable Calculus, Ordinary Differential Equations |
| UAIBA102 | Python Programming | Core | 4 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Programming, Exception Handling |
| UAIBA103 | Digital Electronics | Core | 3 | Number Systems, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits |
| UAIBA104 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs, Sorting and Searching Algorithms |
| UAIBA105 | English for Engineers | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Technical Writing Skills, Oral Communication and Presentation, Professional Ethics |
| UAILB101 | Digital Electronics Lab | Lab | 2 | Logic Gates Implementation, Combinational Logic Design, Sequential Logic Design, Flip-Flops and Latches, Counters and Registers |
| UAILB102 | Python Programming Lab | Lab | 2 | Basic Python Programming, File Operations, Object-Oriented Concepts, Exception Handling, Debugging Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA201 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation |
| UAIBA202 | Object Oriented Programming with C++ | Core | 4 | C++ Fundamentals, Classes and Objects, Inheritance, Polymorphism, Templates and Exception Handling |
| UAIBA203 | Operating Systems | Core | 3 | Operating System Concepts, Process Management, Memory Management, File Systems, Concurrency and Deadlocks |
| UAIBA204 | Database Management Systems | Core | 4 | Database Concepts, Relational Model, SQL Query Language, Database Design and Normalization, Transaction Management |
| UAIBA205 | Environmental Science and Engineering | Core | 2 | Natural Resources and Ecosystems, Biodiversity and Conservation, Environmental Pollution, Solid Waste Management, Sustainable Development |
| UAILB201 | Object Oriented Programming Lab | Lab | 2 | C++ Programming Practice, Class and Object Implementation, Inheritance and Polymorphism, Operator Overloading, File Handling |
| UAILB202 | Database Management Systems Lab | Lab | 2 | SQL Commands and Queries, Database Creation and Manipulation, PL/SQL Programming, Stored Procedures and Triggers, Database Connectivity |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA301 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory and Relations, Functions, Graph Theory, Algebraic Structures |
| UAIBA302 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| UAIBA303 | Artificial Intelligence | Core | 4 | Introduction to AI, Problem Solving Agents, Heuristic Search Techniques, Knowledge Representation, Logical Agents |
| UAIBA304 | Computer Networks | Core | 3 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer |
| UAIBA305 | Universal Human Values | Core | 2 | Self-Exploration, Understanding Harmony, Values in Human Relationships, Values in Society, Holistic Living |
| UAILB301 | Artificial Intelligence Lab | Lab | 2 | Python for AI Applications, Implementation of Search Algorithms, Knowledge Representation Techniques, Logic Programming Basics, Mini AI Projects |
| UAILB302 | Computer Networks Lab | Lab | 2 | Network Configuration, Socket Programming, Protocol Implementation, Network Analysis Tools, Security Configurations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA401 | Computer Architecture | Core | 3 | Computer Organization, CPU Design, Memory Hierarchy, Input/Output Organization, Pipelining |
| UAIBA402 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Model Evaluation and Validation, Neural Networks Basics, Ensemble Methods |
| UAIBA403 | Cloud Computing | Core | 3 | Cloud Concepts and Models, Virtualization Technology, Cloud Services (IaaS, PaaS, SaaS), Cloud Security, Cloud Platforms |
| UAIBA404 | Web Technologies | Core | 3 | HTML and CSS, JavaScript and DOM, Server-Side Scripting, Web Servers and Databases, API Design and Integration |
| UAIEL001 | Professional Elective – I: Soft Computing | Elective | 3 | Fuzzy Logic Systems, Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence, Hybrid Soft Computing Techniques |
| UAILB401 | Machine Learning Lab | Lab | 2 | Data Preprocessing, Implementing ML Algorithms, Model Training and Evaluation, Using Scikit-learn, Visualization of Results |
| UAILB402 | Web Technologies Lab | Lab | 2 | Front-End Web Development, Server-Side Scripting with Databases, AJAX and JSON, Web Services Development, Responsive Design |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA501 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Transfer Learning |
| UAIBA502 | Natural Language Processing | Core | 4 | NLP Fundamentals, Text Preprocessing, Language Models, Machine Translation, Sentiment Analysis |
| UAIBA503 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, Data Warehousing, Data Visualization |
| UAIEL002 | Professional Elective – II: Computer Vision | Elective | 3 | Image Acquisition and Representation, Image Processing Techniques, Feature Extraction, Object Detection and Recognition, 3D Computer Vision |
| UAIPJ501 | Project Work – Phase I | Project | 2 | Problem Identification, Literature Survey, Requirement Analysis, System Design, Prototyping |
| UAILB501 | Deep Learning Lab | Lab | 2 | Implementing CNNs and RNNs, Frameworks like TensorFlow/PyTorch, Image Recognition Tasks, Sequence Generation Models, Model Optimization |
| UAILB502 | Natural Language Processing Lab | Lab | 2 | Text Preprocessing using NLTK, Word Embeddings, Building Chatbots, Sentiment Analysis Applications, Machine Translation Models |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UAIBA601 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning, Deep Reinforcement Learning |
| UAIEL003 | Professional Elective – III: Ethical AI | Elective | 3 | AI Ethics Principles, Bias and Fairness in AI, Transparency and Explainability, Accountability and Governance, Societal Impact of AI |
| UAIPJ601 | Project Work – Phase II | Project | 8 | System Implementation, Testing and Debugging, Performance Evaluation, Project Documentation, Final Presentation |
| UAIPJ602 | Internship | Project | 2 | Real-world Problem Solving, Industry Best Practices, Professional Communication, Report Writing, Skill Application |
