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BE-AIML in Artificial Intelligence Machine Learning at Yenepoya Institute of Technology
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Yenepoya Institute of Technology Dakshina Kannada?
This Artificial Intelligence & Machine Learning (AIML) program at Yenepoya Institute of Technology focuses on equipping students with theoretical knowledge and practical skills in AI, ML, Deep Learning, and Data Science. With India''''s rapidly growing tech sector, the program emphasizes real-world applications and innovation, preparing students for the evolving demands of intelligent systems development and data-driven decision-making across various industries.
Who Should Apply?
This program is ideal for fresh graduates passionate about cutting-edge technology and problem-solving, seeking entry into high-growth fields like AI and Machine Learning. It also caters to aspiring researchers interested in advanced algorithms and intelligent systems. Students with a strong foundation in mathematics and programming from their 10+2 are particularly well-suited, aiming for careers that leverage data and automation.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Specialist, Data Scientist, NLP Engineer, and Computer Vision Engineer in IT, healthcare, and finance sectors. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals earning upwards of INR 15-30 lakhs. The program aligns with industry certifications, fostering continuous growth in a dynamic job market.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant time to mastering C and Java programming, alongside data structures and algorithms. Participate in coding challenges regularly to improve problem-solving skills and logical thinking.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses on DSA
Career Connection
Strong coding and DSA skills are fundamental for technical interviews and crucial for building complex AI/ML models efficiently.
Build a Solid Mathematical Base- (Semester 1-3)
Focus intently on Engineering Mathematics, Discrete Mathematics, and Probability & Statistics. These form the bedrock for understanding AI/ML algorithms, ensuring you grasp the ''''why'''' behind the ''''how''''.
Tools & Resources
Khan Academy, MIT OpenCourseware, textbooks, peer study groups
Career Connection
A robust mathematical understanding is critical for research, advanced algorithm development, and debugging complex ML models.
Engage in Small-Scale Projects- (Semester 2 onwards)
Start building small, personal projects using foundational programming skills. This could be anything from a simple calculator to a basic game, applying learned concepts in a practical setting.
Tools & Resources
GitHub for version control, Python/Java IDEs, online tutorials
Career Connection
Develops problem-solving, debugging skills, and creates an early portfolio, demonstrating practical application of knowledge.
Intermediate Stage
Immerse in Machine Learning & Deep Learning Concepts- (Semester 4-6)
Beyond coursework, actively explore various ML/DL algorithms, frameworks (TensorFlow, PyTorch), and their applications. Participate in Kaggle competitions or build projects to implement these algorithms from scratch.
Tools & Resources
Kaggle, Coursera (Andrew Ng''''s ML/DL courses), Medium articles, GitHub repositories
Career Connection
Direct application of core specialization skills, essential for roles like ML Engineer or Data Scientist. Builds a strong project portfolio.
Develop Strong Data Handling Skills- (Semester 3-5)
Gain expertise in database management (SQL, NoSQL), big data technologies (Hadoop, Spark), and data manipulation libraries (Pandas, NumPy). Practice data cleaning, transformation, and analysis with real datasets.
Tools & Resources
MySQL Workbench, Apache Hadoop, Spark, Google Colab, UCI Machine Learning Repository
Career Connection
Data proficiency is a non-negotiable skill for any AI/ML role, crucial for feature engineering and model training.
Network and Participate in Tech Events- (Semester 3-6)
Attend webinars, workshops, and hackathons organized by the department, VTU, or local tech communities. Connect with industry professionals, alumni, and peers to gain insights and identify opportunities.
Tools & Resources
LinkedIn, college career fair events, local AI/ML meetups
Career Connection
Opens doors to internships, mentorships, and provides exposure to industry trends and potential employers.
Advanced Stage
Undertake Industry-Relevant Projects & Internships- (Semester 6-8)
Seek out internships or collaborate on major projects that address real-world challenges, ideally with industry mentorship. Focus on applying advanced AI/ML techniques to solve complex problems.
Tools & Resources
Company internship portals, college placement cell, industry contacts, project management tools (Jira, Trello)
Career Connection
Provides invaluable practical experience, strengthens resumes, often leads to pre-placement offers, and builds a professional network.
Specialize and Deepen Expertise- (Semester 7-8)
Choose professional electives and project topics that align with your career aspirations (e.g., NLP, Computer Vision, Reinforcement Learning, Generative AI). Pursue certifications in your chosen niche.
Tools & Resources
AWS/Azure/GCP ML certifications, specialized online courses (Coursera, edX), research papers (arXiv)
Career Connection
Positions you as a specialist in a high-demand area, increasing employability and potential for advanced roles.
Refine Soft Skills and Interview Preparation- (Semester 7-8)
Practice communication, presentation, and teamwork skills through seminars, group projects, and mock interviews. Work on resume building, aptitude, and technical interview preparation comprehensively.
Tools & Resources
College career services, mock interview platforms, online aptitude tests, LinkedIn for networking
Career Connection
Ensures you are not only technically proficient but also articulate and well-prepared to secure top placements in leading companies.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject and obtained at least 45% marks (40% for reserved category) in the above subjects taken together. Admission through Karnataka Common Entrance Test (KCET) or JEE Main.
Duration: 8 semesters / 4 years
Credits: 150 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT11 | Engineering Mathematics-I | Core | 4 | Differential Calculus-I, Differential Calculus-II, Partial Differentiation, Multiple Integrals, Vector Calculus |
| 22EEL14/24 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Three-Phase Systems, Electrical Machines, Electrical Safety |
| 22ELN14/24 | Basic Electronics | Core | 3 | Semiconductor Diodes, Transistors, Operational Amplifiers, Digital Electronics, Transducers |
| 22EGH15/25 | Communicative English | Core | 1 | Basic English Grammar, Reading Comprehension, Public Speaking, Technical Writing, Listening Skills |
| 22EGD16/26 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Basics, Development of Surfaces |
| 22CS17/27 | C Programming for Problem Solving | Core | 3 | Introduction to C, Control Structures, Functions, Arrays, Pointers, Structures |
| 22EEL18/28 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Network Theorems, Measurement of Power, RC Circuits, Earthing, Motor Characteristics |
| 22CS19/29 | C Programming Lab | Lab | 1 | C Program Implementation, Debugging, Problem Solving, Data Structures in C, File Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT21 | Engineering Mathematics-II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Inverse Laplace Transforms, Fourier Series, Partial Differential Equations |
| 22PHY22 | Engineering Physics | Core | 4 | Modern Physics, Quantum Mechanics, Solid State Physics, Lasers and Optics, Nanomaterials |
| 22AI23 | System Software & Operating System | Core | 4 | System Software, Operating System Concepts, Process Management, Memory Management, File Systems |
| 22AI24 | Data Structures & Algorithms | Core | 4 | Arrays, Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms |
| 22CIV25 | Professional Skills & Entrepreneurship | Core | 1 | Soft Skills, Communication, Teamwork, Critical Thinking, Entrepreneurship |
| 22PHYL26 | Engineering Physics Lab | Lab | 1 | Photoelectric Effect, LASER Diffraction, Semiconductor Characteristics, Optical Fiber, Ultrasonic Interferometer |
| 22AIL27 | System Software & Operating System Lab | Lab | 1 | Linux Commands, Shell Scripting, Process Management, Memory Allocation, File System Operations |
| 22AIL28 | Data Structures & Algorithms Lab | Lab | 1 | Implementation of Data Structures, Algorithmic Problem Solving, Recursion, Time Complexity Analysis |
| 22EVS29 | Environmental Studies | Core | 2 | Ecosystems, Environmental Pollution, Natural Resources, Biodiversity, Environmental Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI31 | Computer Organization & Architecture | Core | 4 | Basic Computer Organization, CPU Design, Memory Hierarchy, Input/Output Organization, Pipelining |
| 22AI32 | Discrete Mathematics for Computer Science | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| 22AI33 | Object Oriented Programming with Java | Core | 4 | OOP Concepts, Java Basics, Classes and Objects, Inheritance and Polymorphism, Exception Handling |
| 22AI34 | Database Management Systems | Core | 4 | Database Concepts, ER Modeling, Relational Model and SQL, Normalization, Transaction Management |
| 22AIL35 | Object Oriented Programming with Java Lab | Lab | 1 | Java Program Development, OOP Implementation, GUI Programming, Exception Handling, File I/O |
| 22AIL36 | Database Management Systems Lab | Lab | 1 | SQL Queries, Database Design, PL/SQL, Triggers and Views, Stored Procedures |
| 22HSM37 | Aptitude and Logical Reasoning | Core | 2 | Quantitative Aptitude, Logical Reasoning, Verbal Ability, Data Interpretation, Critical Thinking |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI41 | Design & Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Backtracking and Branch & Bound |
| 22AI42 | Machine Learning | Core | 4 | Introduction to ML, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation |
| 22AI43 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems |
| 22AI44 | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 22AIL45 | Machine Learning Lab | Lab | 1 | Python for ML, Data Preprocessing, Implementing Algorithms, Model Training, Evaluation Metrics |
| 22AIL46 | Operating Systems Lab | Lab | 1 | Process Synchronization, Deadlock Avoidance, Memory Management Simulations, File System Implementation |
| 22HSM47 | Universal Human Values | Core | 2 | Introduction to Value Education, Harmony in the Human Being, Harmony in the Family, Harmony in Society, Harmony in Nature |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI51 | Software Engineering | Core | 3 | Software Process Models, Requirements Engineering, Design Concepts, Software Testing, Project Management |
| 22AI52 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce and Spark, Data Warehousing, Data Streaming |
| 22AI53 | Deep Learning | Core | 3 | Neural Networks, Perceptrons and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks |
| 22AI54X | Professional Elective – 1 (e.g., Natural Language Processing) | Elective | 3 | Text Preprocessing, Language Models, Text Classification, Neural NLP, Machine Translation |
| 22AI55X | Open Elective – 1 (e.g., Cloud Computing) | Elective | 3 | Cloud Models, Virtualization, AWS/Azure Basics, Cloud Security, Cloud Services |
| 22AIL56 | Big Data Analytics Lab | Lab | 1 | Hadoop Setup, MapReduce Programs, Spark Implementations, Data Loading, Querying with Hive/Pig |
| 22AIL57 | Deep Learning Lab | Lab | 1 | TensorFlow/PyTorch, CNN/RNN Implementation, Image Classification, Sequence Prediction, Transfer Learning |
| 22AIMIN58 | Mini Project | Project | 2 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI61 | Cyber Security & Forensics | Core | 3 | Introduction to Cyber Security, Cryptography, Network Security, Digital Forensics, Cyber Laws |
| 22AI62 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Q-Learning, Deep Reinforcement Learning |
| 22AI63 | Ethics in AI | Core | 3 | Ethical Frameworks, Bias in AI, Privacy Concerns, AI Safety, Societal Impact of AI |
| 22AI64X | Professional Elective – 2 (e.g., Generative AI) | Elective | 3 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Text-to-Image Generation, Creative AI Applications |
| 22AI65X | Open Elective – 2 (e.g., Entrepreneurship Development) | Elective | 3 | Concept of Entrepreneurship, Business Plan, Startup Ecosystem, Funding and Venture Capital, Marketing Strategies |
| 22AIL66 | Reinforcement Learning Lab | Lab | 1 | OpenAI Gym, Policy Iteration, Value Iteration, Q-Learning Implementation, Deep Q-Networks |
| 22AIHS67 | Research Methodology & IPR | Core | 2 | Research Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights, Patents and Copyrights |
| 22AIINT68 | Internship | Internship | 2 | Industry Exposure, Project Work, Professional Skill Development, Report Submission, Work Ethics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI71 | Cloud Computing for AI | Core | 3 | Cloud Architectures, Virtualization, IaaS/PaaS/SaaS, Cloud ML Platforms (AWS SageMaker, Azure ML), Serverless AI |
| 22AI72X | Professional Elective – 3 (e.g., Digital Image Processing) | Elective | 3 | Image Enhancement, Image Filtering, Image Segmentation, Feature Extraction, Image Compression |
| 22AI73X | Professional Elective – 4 (e.g., Robotics & Automation) | Elective | 3 | Robot Kinematics, Robot Sensors and Actuators, Motion Planning, Robot Control, Industrial Automation |
| 22AI74X | Open Elective – 3 (e.g., Total Quality Management) | Elective | 3 | Quality Principles, TQM Tools and Techniques, Quality Function Deployment, Six Sigma, Process Improvement |
| 22AIP75 | Project Work - Phase 1 | Project | 3 | Problem Definition, Literature Review, System Design, Methodology, Initial Implementation |
| 22AIS76 | Technical Seminar | Seminar | 3 | Research Topic Selection, Literature Survey, Presentation Skills, Technical Report Writing, Question and Answer Session |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIP81 | Project Work - Phase 2 | Project | 6 | Advanced Implementation, Testing and Evaluation, Project Management, Final Report Preparation, Viva Voce |
| 22AIIP82 | Internship / Industrial Practice / Project Work | Internship/Project | 6 | Real-world Problem Solving, Industrial Application, Teamwork and Communication, Professional Ethics, Industry Report Submission |
