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B-TECH in Artificial Intelligence at University of Kerala
Thiruvananthapuram, Kerala
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About the Specialization
What is Artificial Intelligence at University of Kerala Thiruvananthapuram?
This Artificial Intelligence and Data Science program at University of Kerala focuses on developing expertise in machine learning, deep learning, big data analytics, and intelligent systems. It addresses the growing demand for skilled professionals in India''''s rapidly evolving tech landscape, offering a unique blend of theoretical knowledge and practical application. The program emphasizes ethical AI principles and real-world problem-solving, preparing students for innovation.
Who Should Apply?
This program is ideal for fresh science or engineering graduates with a strong aptitude for mathematics and programming, seeking entry into the AI and data science fields. It also suits working professionals looking to transition or upskill in advanced analytics, and career changers aiming for roles in intelligent automation or data-driven decision making. A foundational understanding of computing concepts is beneficial.
Why Choose This Course?
Graduates of this program can expect to secure diverse roles as AI engineers, data scientists, machine learning specialists, or big data analysts across various sectors in India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The curriculum aligns with modern industry demands, preparing students for professional certifications and fostering strong growth trajectories in leading Indian companies and startups.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate extensive time to deeply understand core programming languages like C and Python. Consistently practice coding challenges on platforms such as HackerRank and LeetCode to build robust problem-solving skills, which are crucial for future algorithm development in AI.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL Programming Courses
Career Connection
Strong coding fundamentals are the bedrock for any tech role in India, directly impacting success in technical interviews and project development.
Build a Strong Mathematical and Logical Base- (Semester 1-3)
Focus on developing a deep understanding of Engineering Mathematics I & II, and Discrete Mathematical Structures. Utilize online resources like Khan Academy or NPTEL to clarify complex concepts. A solid mathematical foundation is indispensable for comprehending AI algorithms and data science principles effectively.
Tools & Resources
Khan Academy, NPTEL Mathematics Courses, University Textbooks
Career Connection
Mastering these foundational concepts enhances analytical thinking, vital for research and advanced roles in AI/ML, and improves problem-solving capabilities.
Engage in Peer Learning and Contests- (Semester 1-2)
Form active study groups to discuss complex topics, collaboratively solve problems, and clarify doubts. Actively participate in college-level coding contests and hackathons, often organized by campus chapters of CodeChef or ACM, to boost competitive programming skills and teamwork.
Tools & Resources
Study Groups, CodeChef, HackerEarth, College Tech Clubs
Career Connection
Develops teamwork, communication, and problem-solving under pressure, highly valued skills by Indian tech companies during campus recruitment drives.
Intermediate Stage
Practical Application through Mini-Projects- (Semester 3-5)
Beyond routine lab work, initiate and complete self-driven mini-projects using Python libraries like NumPy, Pandas, and Scikit-learn. Contribute to open-source projects on platforms like GitHub or participate in university-level hackathons to apply concepts learned in Data Structures, OOP, and Machine Learning to real-world scenarios.
Tools & Resources
Python (NumPy, Pandas, Scikit-learn), GitHub, Kaggle, Jupyter Notebooks
Career Connection
Building a portfolio of practical projects is crucial for demonstrating applied skills to potential employers in India''''s competitive job market.
Seek Early Industry Exposure- (Semester 4-5)
Actively attend webinars, workshops, and tech talks conducted by industry experts and professionals. Look for opportunities to complete summer internships, even short-term ones, in local startups or research labs to understand real-world AI challenges, industry trends, and professional work environments.
Tools & Resources
LinkedIn, Company Websites, University Career Fairs, Industry Conferences
Career Connection
Early industry exposure provides invaluable insights, helps in career path selection, and can lead to pre-placement offers or networking opportunities within the Indian tech ecosystem.
Specialize and Network Proactively- (Semester 4-5)
Identify specific areas within AI/DS, such as Natural Language Processing or Computer Vision, that deeply interest you. Join professional online communities (e.g., LinkedIn groups, Kaggle forums) and connect with alumni for mentorship, career guidance, and insights into specific industry pathways in India.
Tools & Resources
LinkedIn, Kaggle Forums, Specialized Online Courses, University Alumni Network
Career Connection
Niche specialization makes you a more attractive candidate for specific roles, while networking opens doors to opportunities and mentorship from experienced professionals.
Advanced Stage
Intensive Placement and Interview Preparation- (Semester 6-8)
Dedicate significant time to rigorous technical interview preparation, focusing on advanced data structures, algorithms, system design, and in-depth AI/ML concepts. Participate in mock interviews and continuously refine a strong project portfolio to effectively showcase skills to leading Indian and multinational tech companies during placement drives.
Tools & Resources
InterviewBit, GeeksforGeeks Interview Prep, LinkedIn Learning, Mock Interviews
Career Connection
Thorough preparation directly translates into securing highly sought-after placements and internships in top-tier tech companies across India.
Deep Dive into Specialization and Research- (Semester 7-8)
Choose a niche within AI/DS, such as Deep Learning architectures, Reinforcement Learning applications, or Ethical AI, for advanced study. Consider contributing to a research paper, participating in university-level research projects, or attending national and international conferences to gain cutting-edge knowledge and academic exposure.
Tools & Resources
Research Papers (e.g., arXiv), Conferences (e.g., AAAI, ICML), University Research Labs
Career Connection
Advanced specialization opens doors to R&D roles, academic pursuits, and positions requiring expertise in highly specialized areas of AI within Indian and global firms.
Develop Leadership and Professional Skills- (Semester 6-8)
Actively seek leadership roles within student clubs, technical societies, or event organizing committees. Continuously refine presentation, negotiation, and overall communication skills. These crucial soft skills are highly valued for professional growth and leadership positions in the dynamic Indian IT sector, complementing technical expertise.
Tools & Resources
Toastmasters, College Leadership Programs, Public Speaking Workshops, Professional Development Courses
Career Connection
Enhances employability, accelerates career progression into managerial or team lead roles, and builds a well-rounded professional profile attractive to employers.
Program Structure and Curriculum
Eligibility:
- As per University of Kerala B.Tech admission norms (typically 10+2 with Physics, Chemistry, and Mathematics)
Duration: 8 semesters / 4 years
Credits: 154 Credits
Assessment: Internal: 40% (for theory courses), 50% (for lab/project courses), External: 60% (for theory courses), 50% (for lab/project courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BS101 | Engineering Mathematics I | Core | 4 | Differential Calculus, Integral Calculus, Ordinary Differential Equations, Partial Derivatives, Multiple Integrals |
| 20BS102 | Engineering Physics | Core | 3 | Oscillations and Waves, Quantum Mechanics, Solid State Physics, Lasers and Photonics, Fiber Optics |
| 20BS103 | Engineering Chemistry | Core | 3 | Basic Concepts in Chemistry, Chemical Bonding, Thermodynamics, Electrochemistry, Organic Chemistry |
| 20HS101 | Life Skills | Core | 3 | Self-Awareness & Self-Management, Communication Skills, Leadership Skills, Professional Ethics, Social Responsibility |
| 20ES101 | Engineering Graphics | Core | 3 | Lines and Planes, Solids, Orthographic Projections, Isometric Projections, Sectional Views |
| 20BS104 | Engineering Physics Lab | Lab | 1 | Experiments on Optics, Electricity, Mechanics, Material Properties |
| 20BS105 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Titrations, pH Measurements, Viscosity |
| 20ES102 | C Programming Lab | Lab | 2 | C Language Basics, Control Structures, Arrays, Functions, Pointers |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20BS201 | Engineering Mathematics II | Core | 4 | Laplace Transforms, Fourier Series, Vector Calculus, Complex Numbers, Probability Distributions |
| 20ES201 | Basic Civil & Mechanical Engineering | Core | 4 | Building Materials, Surveying, Thermodynamics, IC Engines, Manufacturing Processes |
| 20ES202 | Basic Electrical & Electronics Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, Diodes & Transistors, Digital Electronics |
| 20HS201 | Professional Communication | Core | 3 | Principles of Communication, Presentation Skills, Technical Writing, Group Discussion, Interview Skills |
| 20ES203 | Engineering Workshop | Lab | 2 | Carpentry, Fitting, Welding, Foundry, Sheet Metal |
| 20ES204 | Electrical & Electronics Lab | Lab | 2 | Basic Electrical Circuits, Diode Characteristics, Transistor Amplifiers, Logic Gates |
| 20CC201 | Sustainable Engineering | Core | 1 | Environmental Impact, Renewable Energy, Green Building, Life Cycle Assessment, Waste Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI301 | Discrete Mathematical Structures | Core | 4 | Set Theory & Logic, Relations & Functions, Graph Theory, Algebraic Structures, Boolean Algebra |
| 20AI302 | Data Structures & Algorithms | Core | 4 | Arrays, Linked Lists, Stacks & Queues, Trees & Graphs, Sorting & Searching, Hashing |
| 20AI303 | Object Oriented Programming | Core | 3 | Classes & Objects, Inheritance, Polymorphism, Encapsulation, Exception Handling |
| 20AI304 | Digital Logic Design | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory Elements |
| 20AI305 | Computer Organization & Architecture | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Pipelining, Instruction Sets |
| 20AI306 | Data Structures Lab | Lab | 1 | Implementation of Lists, Stacks & Queues, Tree & Graph Traversals, Sorting Algorithms, Searching Techniques |
| 20AI307 | Object Oriented Programming Lab | Lab | 1 | Object-oriented concepts using Java/Python, Inheritance & Polymorphism, File Handling, GUI Programming |
| 20HS301 | Professional Ethics | Core | 1 | Engineering Ethics, Professionalism, Moral Dilemmas, Cyber Ethics, Environmental Ethics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI401 | Probability & Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Sampling Theory, Hypothesis Testing |
| 20AI402 | Design & Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 20AI403 | Database Management Systems | Core | 3 | Relational Model, SQL, ER Diagrams, Normalization, Transaction Management |
| 20AI404 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Concurrency |
| 20AI405 | Microprocessors & Microcontrollers | Core | 3 | 8086 Architecture, Instruction Set, Memory Interfacing, I/O Interfacing, Microcontrollers |
| 20AI406 | Database Management Systems Lab | Lab | 1 | SQL Queries, Database Design, PL/SQL Programming, Frontend Connectivity |
| 20AI407 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Creation, Thread Synchronization, Memory Allocation |
| 20HS401 | Industrial Economics & Foreign Trade | Core | 1 | Basic Economic Principles, Demand & Supply, Market Structures, International Trade, Macroeconomics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI501 | Artificial Intelligence | Core | 4 | AI Agents, Search Algorithms, Knowledge Representation, Logic Programming, Expert Systems |
| 20AI502 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression, Classification, Model Evaluation |
| 20AI503 | Data Mining & Warehousing | Core | 3 | Data Preprocessing, Data Warehousing, OLAP, Association Rules, Clustering |
| 20AI504 | Computer Networks | Core | 3 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Protocols, Network Security |
| 20AI5E0x | Professional Elective I (e.g., Neural Networks & Deep Learning) | Elective | 3 | Perceptrons, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, TensorFlow/PyTorch Basics |
| 20AI506 | Machine Learning Lab | Lab | 1 | Implementation of ML algorithms, Data preprocessing, Model training & Evaluation, Feature Engineering |
| 20AI507 | AI & Data Mining Lab | Lab | 1 | AI search algorithms, Knowledge representation techniques, Data mining tools (e.g., Weka), Data visualization |
| 20AI508 | Mini Project I | Project | 1 | Problem Definition, Design & Planning, Implementation & Testing, Report Writing |
| 20HS501 | Business Management & Ethics | Core | 1 | Management Principles, Organizational Behavior, Marketing, Financial Management, Ethical Decision Making |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI601 | Advanced Machine Learning | Core | 4 | Ensemble Methods, Dimensionality Reduction, Reinforcement Learning, Generative Models, Time Series Analysis |
| 20AI602 | Natural Language Processing | Core | 4 | Text Preprocessing, N-grams, Word Embeddings, POS Tagging, Sentiment Analysis |
| 20AI603 | Computer Vision | Core | 3 | Image Processing Basics, Feature Detection, Object Recognition, Image Segmentation, Deep Learning for Vision |
| 20AI604 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, MapReduce, Spark, NoSQL Databases, Stream Processing |
| 20AI6E0x | Professional Elective II (e.g., Cloud Computing for AI) | Elective | 3 | AWS/Azure/GCP AI Services, Cloud Infrastructure, Serverless AI, Data Lakes, Cloud Security |
| 20AI605 | Natural Language Processing Lab | Lab | 1 | NLTK/SpaCy usage, Text analysis techniques, Chatbot development, Information Extraction |
| 20AI606 | Computer Vision Lab | Lab | 1 | OpenCV basics, Image manipulation, Object detection models, Feature Extraction |
| 20AI607 | Mini Project II | Project | 1 | Advanced AI/DS project, Data Collection & Preprocessing, Model Development & Deployment, Performance Evaluation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI701 | Deep Learning | Core | 4 | Neural Network Architectures, CNNs & RNNs, LSTMs & Transformers, Generative Adversarial Networks, Transfer Learning |
| 20AI702 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Value Iteration, Policy Iteration, Q-Learning, Deep Reinforcement Learning |
| 20AI7E0x | Professional Elective III (e.g., Ethical AI) | Elective | 3 | Bias in AI, AI Governance, Data Privacy, Fairness & Transparency, AI Ethics Frameworks |
| 20AI7O0x | Open Elective I (e.g., Introduction to IoT) | Elective | 3 | IoT Architecture, Sensors & Actuators, Communication Protocols, Data Analytics for IoT, IoT Security |
| 20AI703 | Deep Learning Lab | Lab | 1 | Implement DL models with Keras/PyTorch, Fine-tuning techniques, Transfer learning applications, Model Optimization |
| 20AI704 | Project Phase I | Project | 4 | Literature Survey, Problem Definition, System Design, Initial Implementation, Presentation |
| 20HS701 | Industrial Training / Internship | Internship | 1 | On-the-job experience, Industry practices, Professional skills development, Workplace communication |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20AI8E0x | Professional Elective IV (e.g., AI in Healthcare) | Elective | 3 | Medical Imaging Analysis, Drug Discovery with AI, Clinical Decision Support Systems, Personalized Medicine, Ethical considerations in Healthcare AI |
| 20AI8O0x | Open Elective II (e.g., Entrepreneurship Development) | Elective | 3 | Business Plan Development, Start-up Ecosystem, Funding & Investments, Marketing Strategies, Legal Aspects of Business |
| 20AI801 | Project Phase II | Project | 9 | Advanced Implementation, Testing & Validation, Optimization Techniques, Final Report Preparation, Viva Voce |
| 20AI802 | Comprehensive Viva Voce | Viva | 0 | Overall knowledge assessment, Core concepts in AI/DS, Understanding of engineering principles |
