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B-SC in Computer Science With Cognitive Systems at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science with Cognitive Systems at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Computer Science with Cognitive Systems program at SRM Institute of Science and Technology focuses on equipping students with a robust foundation in computer science combined with the intricacies of artificial intelligence, machine learning, and human-computer interaction. It addresses the growing demand in the Indian market for professionals capable of building intelligent systems, ranging from predictive analytics to natural language processing solutions. The program distinguishes itself by integrating core computer science principles with cutting-edge cognitive computing paradigms, preparing graduates for a future-ready tech landscape.
Who Should Apply?
This program is ideal for fresh graduates from the 10+2 pattern who possess a strong aptitude for mathematics, logical reasoning, and an inherent curiosity about artificial intelligence and its applications. It also caters to aspiring innovators and problem-solvers eager to delve into the ethical and practical aspects of cognitive systems. A basic understanding of computer science concepts would be beneficial, but the curriculum is designed to build foundational knowledge for individuals transitioning into this specialized field.
Why Choose This Course?
Graduates of this program can expect to pursue exciting career paths in India, including roles such as AI Engineer, Machine Learning Specialist, Data Scientist, Cognitive Systems Developer, and NLP Engineer. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals commanding upwards of INR 10-20 LPA depending on skill and company. The program also aligns with certifications in AI/ML from platforms like Coursera and edX, enhancing career growth in leading Indian and global tech firms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate ample time to mastering C and Python programming, focusing on core concepts, data types, control flow, and problem-solving logic. Regularly practice coding challenges to build a strong base.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, Python documentation
Career Connection
Strong programming fundamentals are critical for passing technical rounds in placements and for building any complex software or AI system.
Understand Digital Logic and Data Structures- (Semester 1-2)
Grasp the underlying hardware principles (Digital Logic) and fundamental data organization techniques (Data Structures). These are the building blocks for efficient software and understanding how computers operate.
Tools & Resources
Logic.ly simulator, online tutorials for data structure visualizations, competitive programming platforms
Career Connection
Essential for roles in embedded systems, system programming, and for optimizing algorithms in any domain.
Develop Effective Study Habits & Peer Learning- (Semester 1-2)
Form study groups, discuss complex topics with peers, and clarify doubts promptly. Attend all lectures and labs, and consistently review notes. Seek guidance from faculty when needed.
Tools & Resources
Google Meet, Discord for group discussions, university library resources, faculty office hours
Career Connection
Fosters collaboration, communication, and problem-solving skills, highly valued in team-based industry environments.
Intermediate Stage
Build a Strong Portfolio with DBMS & Java Projects- (Semester 3-4)
Apply concepts learned in DBMS and Java by building small-scale projects. Create a relational database for a simple application and develop a Java application with OOP principles.
Tools & Resources
MySQL, PostgreSQL, Oracle SQL Developer, IntelliJ IDEA, Eclipse, GitHub for version control
Career Connection
A strong project portfolio demonstrates practical skills to potential employers and is crucial for technical interviews.
Explore AI/ML Concepts Through Mini-Projects & Competitions- (Semester 4-5)
As AI and Machine Learning are introduced, start experimenting with small datasets and open-source libraries. Participate in Kaggle competitions or hackathons to apply theoretical knowledge to real-world problems.
Tools & Resources
Python (Scikit-learn, Pandas, NumPy), TensorFlow, Keras, Kaggle, Hugging Face, Google Colab
Career Connection
Develops critical problem-solving skills, familiarizes with industry-standard tools, and creates visible achievements for resumes.
Network and Seek Industry Exposure- (Semester 3-5)
Attend webinars, workshops, and tech talks organized by the department or industry bodies. Connect with alumni and professionals on LinkedIn to understand industry trends and potential career paths.
Tools & Resources
LinkedIn, industry meetups (virtual/physical), SRMIST alumni network
Career Connection
Opens doors for internships, mentorships, and provides insights into industry demands, improving placement prospects.
Advanced Stage
Specialize and Deepen Knowledge in Cognitive Systems- (Semester 5-6)
Focus on Deep Learning and Natural Language Processing. Engage in advanced projects, research papers, or specialized online courses to gain expertise in a niche area within cognitive systems.
Tools & Resources
PyTorch, TensorFlow, spaCy, NLTK, Hugging Face Transformers, research papers, specialized MOOCs
Career Connection
Builds a strong specialization, making graduates highly competitive for specific AI/ML roles in top companies and research institutions.
Intensive Placement Preparation & Mock Interviews- (Semester 6)
Regularly practice technical questions (coding, algorithms, data structures, core CS subjects) and behavioral interview questions. Participate in mock interviews conducted by the placement cell or peer groups.
Tools & Resources
InterviewBit, GeeksforGeeks, Glassdoor, company-specific interview experiences
Career Connection
Crucial for converting interviews into job offers, ensuring readiness for the competitive Indian job market.
Undertake a Comprehensive Major Project- (Semester 6)
Apply all accumulated knowledge to a significant project that solves a real-world problem using cognitive systems. Document thoroughly, ensuring a robust design, implementation, and evaluation.
Tools & Resources
relevant AI/ML frameworks, cloud platforms (AWS, Azure, GCP), project management tools
Career Connection
The major project showcases advanced problem-solving, technical depth, and research capabilities, serving as a powerful resume booster and discussion point in interviews.
Program Structure and Curriculum
Eligibility:
- A Pass in Higher Secondary Examination (10+2 pattern) or its equivalent examination with Physics, Chemistry, Mathematics / Computer Science / Statistics / Business Mathematics as subjects.
Duration: 6 semesters / 3 years
Credits: 137 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH101J | Communicative English | Foundation | 3 | Language Skills, Grammar and Usage, Vocabulary Building, Reading Comprehension, Written Communication |
| 21LAD101J | Aptitude I | Foundation | 1 | Numerical Ability, Reasoning Skills, Verbal Aptitude, Data Interpretation, Problem-solving Techniques |
| 21CSS101J | Programming in C | Core | 4 | C Language Fundamentals, Control Structures, Functions and Pointers, Arrays and Strings, Structures and File Handling |
| 21CSS102J | Digital Logic and Computer Organization | Core | 4 | Digital Logic Gates, Boolean Algebra, Combinational Circuits, Sequential Circuits, Computer Architecture Basics |
| 21CSS103J | Problem Solving and Programming in C Lab | Lab | 2 | C Programming Exercises, Debugging Techniques, Algorithm Implementation, Flowchart Design, Practical Problem Solving |
| 21CSS104J | Digital Logic Lab | Lab | 1 | Logic Gate Implementation, Boolean Expression Simplification, Combinational Circuit Design, Sequential Circuit Design, Digital System Testing |
| 21CSS105J | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Vector Spaces, Matrices and Determinants, Eigenvalues and Eigenvectors |
| 21CHY101J | Environmental Science | Foundation | 4 | Natural Resources, Ecosystems and Biodiversity, Environmental Pollution, Global Environmental Issues, Environmental Protection |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSS201J | Python Programming | Core | 4 | Python Language Basics, Data Types and Structures, Control Flow and Functions, Object-Oriented Programming in Python, File Handling and Modules |
| 21CSS202J | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching and Hashing |
| 21CSS203J | Operating Systems | Core | 4 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File and I/O Systems |
| 21CSS204J | Python Programming Lab | Lab | 2 | Python Scripting, Data Manipulation, Function Implementation, Object-Oriented Programming Practice, Practical Applications |
| 21CSS205J | Data Structures Lab | Lab | 2 | Implementation of Data Structures, Algorithm Design, Problem Solving using DS, Performance Analysis, Practical Coding Exercises |
| 21LEH201J | Professional Communication | Foundation | 3 | Business Correspondence, Report Writing, Presentation Skills, Interview Techniques, Group Discussion Strategies |
| 21LAD201J | Aptitude II | Foundation | 1 | Advanced Numerical Aptitude, Logical Reasoning Puzzles, Analytical Skills, Critical Thinking, Quantitative Problem Solving |
| 21LSS201J | Value Education | Foundation | 3 | Ethics and Morals, Human Values, Social Responsibility, Professional Ethics, Personality Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSS301J | Discrete Mathematics | Core | 4 | Set Theory, Mathematical Logic, Relations and Functions, Graph Theory, Combinatorics and Recurrence Relations |
| 21CSS302J | Database Management Systems | Core | 4 | DBMS Architecture, Relational Model, SQL Queries, Normalization, Transaction and Concurrency Control |
| 21CSS303J | Computer Networks | Core | 4 | Network Models (OSI/TCP-IP), Network Protocols, IP Addressing and Routing, Data Link Layer, Network Security Basics |
| 21CSS304J | Object Oriented Programming using Java | Core | 4 | OOP Concepts, Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Exception Handling |
| 21CSS305J | Database Management Systems Lab | Lab | 2 | SQL Query Practice, Database Design, PL/SQL Programming, Triggers and Views, Stored Procedures |
| 21CSS306J | Object Oriented Programming using Java Lab | Lab | 2 | Java Programming Exercises, OOP Implementation, GUI Application Development, Error Handling in Java, Practical Coding |
| 21CSS307J | Advanced Aptitude | Skill Enhancement Course | 1 | Quantitative Aptitude, Logical Reasoning, Verbal Reasoning, Data Sufficiency, Interview Preparation |
| 21CSS308J | Professional Skill Development - I | Skill Enhancement Course | 2 | Communication Skills, Interpersonal Skills, Teamwork and Collaboration, Presentation Techniques, Problem-solving |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSS401J | Artificial Intelligence | Core | 4 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Fundamentals |
| 21CSS402J | Web Technology | Core | 4 | HTML5 and CSS3, JavaScript Fundamentals, Client-Side Scripting, Server-Side Programming, Web Services and APIs |
| 21CSS403J | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management |
| 21CSS404J | Artificial Intelligence Lab | Lab | 2 | Python for AI, Search Algorithm Implementation, Knowledge Representation Practice, Logic Programming, AI Tools Exploration |
| 21CSS405J | Web Technology Lab | Lab | 2 | HTML/CSS Website Design, JavaScript Interactivity, Server-Side Scripting Practice, Database Integration for Web, Web Application Development |
| 21CSS406J | Minor Project - I | Project | 2 | Project Planning, System Design, Implementation Phases, Documentation, Presentation Skills |
| 21CSS407J | Industry Specific Technical Aptitude - I | Skill Enhancement Course | 1 | Industry Trends, Technical Problem Solving, Domain-Specific Challenges, Case Studies, Current Technologies |
| 21CSS408J | Professional Skill Development - II | Skill Enhancement Course | 2 | Leadership Qualities, Negotiation Skills, Conflict Resolution, Time Management, Decision Making |
| 21CSE411J | Computer Graphics | Elective | 2 | Graphics Pipeline, 2D/3D Transformations, Viewing and Clipping, Illumination Models, OpenGL Basics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSS501J | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Techniques, Neural Networks Fundamentals |
| 21CSS502J | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, MapReduce Framework, Spark Architecture, Data Warehousing |
| 21CSS503J | Cryptography and Network Security | Core | 4 | Cryptographic Fundamentals, Symmetric Key Cryptography, Public Key Cryptography, Hash Functions and Digital Signatures, Network Security Protocols |
| 21CSS504J | Machine Learning Lab | Lab | 2 | Python for ML, Scikit-learn Practice, Data Preprocessing, Model Training and Evaluation, TensorFlow/Keras Basics |
| 21CSS505J | Big Data Analytics Lab | Lab | 2 | Hadoop Setup and Commands, MapReduce Programming, Spark Applications, Data Analysis Tools, Distributed Data Processing |
| 21CSS506J | Minor Project - II | Project | 2 | Advanced Project Planning, System Design and Architecture, Implementation and Testing, Technical Report Writing, Team Collaboration |
| 21CSS507J | Industry Specific Technical Aptitude - II | Skill Enhancement Course | 1 | Advanced Technical Problem Solving, Industry Domain Knowledge, Emerging Technologies, Case Study Analysis, Interview Strategies |
| 21CSS508J | Professional Skill Development - III | Skill Enhancement Course | 2 | Entrepreneurship Skills, Innovation and Creativity, Ethical Hacking Awareness, Cloud Computing Basics, Data Privacy and Security |
| 21CSE415J | Image Processing | Elective | 2 | Digital Image Fundamentals, Image Enhancement, Image Restoration, Image Segmentation, Feature Extraction |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CSS601J | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Generative Adversarial Networks (GANs) |
| 21CSS602J | Natural Language Processing | Core | 4 | NLP Fundamentals, Text Preprocessing, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| 21CSS603J | Project Work | Project | 8 | Comprehensive Project Planning, System Architecture Design, Advanced Implementation, Testing and Validation, Technical Report and Presentation |
| 21CSS604J | Deep Learning and NLP Lab | Lab | 2 | TensorFlow/PyTorch Implementation, CNN/RNN Application, Text Classification Tasks, Sequence Generation, NLP Toolkit Usage |
| 21CSS605J | Industry Specific Technical Aptitude - III | Skill Enhancement Course | 2 | Mock Interviews, Group Discussion Practice, Resume Building, Career Counseling, Professional Networking |
| 21CSE412J | Cloud Computing | Elective | 2 | Cloud Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security |
