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B-E in Artificial Intelligence Machine Learning at Sahyadri College of Engineering & Management
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Sahyadri College of Engineering & Management Dakshina Kannada?
This Artificial Intelligence & Machine Learning program at Sahyadri College of Engineering & Management focuses on developing core competencies in AI, ML, Deep Learning, and Data Science. With a curriculum aligned to industry needs, it equips students to tackle complex problems using cutting-edge technologies. The Indian industry is experiencing exponential growth in AI/ML adoption, making this specialization highly relevant for future careers.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics, programming, and problem-solving, seeking entry into high-growth tech domains. It suits fresh 10+2 graduates aiming for careers in data science, AI development, or machine learning engineering. Professionals looking to upskill or career changers transitioning into AI/ML can also benefit from its comprehensive foundation.
Why Choose This Course?
Graduates of this program can expect diverse career paths such as AI Engineer, Machine Learning Scientist, Data Scientist, or Robotics Engineer in Indian tech giants, startups, and research institutions. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The curriculum also prepares students for global certifications in AI/ML and fosters innovation.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Build a strong base in C/C++ and Python, focusing on data structures and algorithms. Regularly practice coding problems on platforms like HackerRank, GeeksforGeeks, and CodeChef to solidify logic and problem-solving skills, which are crucial for all advanced AI/ML courses.
Tools & Resources
CodeChef, GeeksforGeeks, HackerRank
Career Connection
Strong programming fundamentals are essential for cracking technical interviews and building efficient AI/ML models.
Excel in Mathematics and Statistics- (Semester 1-2)
Dedicate extra time to understand Calculus, Linear Algebra, Probability, and Statistics. Utilize online resources like Khan Academy, NPTEL, and YouTube channels to grasp concepts, as these form the bedrock of machine learning algorithms and data analysis.
Tools & Resources
Khan Academy, NPTEL, MIT OpenCourseware
Career Connection
A robust mathematical background is key for understanding AI/ML model intricacies and developing novel algorithms.
Engage in Peer Learning Groups- (Semester 1-2)
Form study groups to discuss complex topics, solve assignments collaboratively, and clarify doubts. Participating in college-level coding competitions in these early semesters helps build confidence, fosters teamwork, and introduces competitive programming scenarios.
Tools & Resources
Study groups, Competitive programming platforms
Career Connection
Develops problem-solving skills, teamwork, and a competitive edge valuable in the tech industry.
Intermediate Stage
Build a Strong Data Science Portfolio- (Semester 3-5)
Actively participate in Kaggle competitions or work on mini-projects involving real-world datasets. Focus on data cleaning, visualization, and basic machine learning model implementation using Python libraries (Pandas, NumPy, Scikit-learn). Document your work on GitHub.
Tools & Resources
Kaggle, GitHub, Jupyter Notebook, Python libraries
Career Connection
A strong portfolio demonstrates practical skills and project experience, crucial for internships and job applications.
Seek Early Industry Exposure- (Semester 3-5)
Look for internships or workshops during semester breaks in data analytics, web development, or basic AI/ML roles. This exposure helps connect theoretical knowledge with practical applications, builds a professional network, and provides insights into industry demands.
Tools & Resources
College placement cell, LinkedIn, Internshala
Career Connection
Gains real-world experience, mentorship, and increases chances of pre-placement offers.
Specialize in Key ML Areas- (Semester 3-5)
Identify areas of interest like Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning early. Take relevant online courses (Coursera, edX) and engage in department-level research projects or hackathons to deepen expertise in your chosen specialization.
Tools & Resources
Coursera, edX, NPTEL, Google AI/ML resources
Career Connection
Develops specialized skills highly sought after in specific AI/ML roles and research positions.
Advanced Stage
Focus on Advanced AI/ML Projects- (Semester 6-8)
Undertake a substantial final year project that solves a real-world problem using advanced AI/ML techniques (Deep Learning, Reinforcement Learning, MLOps). Document the project thoroughly, including methodology, implementation, and results, making it presentation-ready.
Tools & Resources
TensorFlow, PyTorch, AWS/Azure ML, GitHub
Career Connection
Showcases advanced problem-solving, implementation skills, and contributes significantly to your resume for top-tier roles.
Prepare for Placements Strategically- (Semester 6-8)
Start preparing for technical interviews, aptitude tests, and group discussions well in advance. Focus on data structures, algorithms, system design, and AI/ML specific questions. Utilize college placement cells, mock interviews, and online platforms like LeetCode and InterviewBit.
Tools & Resources
LeetCode, InterviewBit, College placement training, Mock interviews
Career Connection
Ensures readiness for campus placements and off-campus opportunities in leading tech companies.
Network and Professional Development- (Semester 6-8)
Attend national and international conferences, seminars, and workshops related to AI/ML. Engage with industry professionals, join LinkedIn communities, and consider professional certifications to enhance your resume and stay updated with industry trends and networking opportunities.
Tools & Resources
LinkedIn, Professional conferences (e.g., CVPR, NeurIPS, ICDM), Industry certifications
Career Connection
Expands professional network, provides insights into latest industry trends, and opens doors to new opportunities.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 / PUC with Physics and Mathematics as compulsory subjects along with one of Chemistry/Biotechnology/Biology/Electronics/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies. Obtained a minimum aggregate score as prescribed by VTU/AICTE/Government of Karnataka.
Duration: 8 semesters / 4 years
Credits: 152 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT11 | Calculus and Differential Equations | Basic Science | 4 | Differential Calculus, Integral Calculus, Partial Differential Equations, Multiple Integrals, Vector Calculus |
| 21PHY12 | Engineering Physics | Basic Science | 4 | Quantum Mechanics, Lasers, Optical Fibers, Dielectric Materials, Magnetic Materials |
| 21PCD13 | Programming for Problem Solving | Engineering Science | 3 | Introduction to C Programming, Control Structures, Functions, Arrays and Strings, Pointers and Structures |
| 21ELN14 | Basic Electronics | Engineering Science | 3 | Semiconductor Diodes, BJT Amplifiers, Operational Amplifiers, Digital Logic Circuits, Electronic Measuring Instruments |
| 21CIV15 | Basic Civil and Mechanical Engineering | Engineering Science | 3 | Civil Engineering Materials, Building Construction, Surveying, Thermodynamics, Internal Combustion Engines, Power Transmission |
| 21PCD16 | Programming for Problem Solving Lab | Lab | 1 | C Program Implementation, Conditional Statements and Loops, Functions and Arrays, Strings and Pointers, Structures and File I/O |
| 21PHY17 | Engineering Physics Lab | Lab | 1 | Young''''s Modulus Experiment, Photoelectric Effect, LASER Diffraction, Dielectric Constant Measurement |
| 21CPE18 | Computer Aided Engineering Graphics | Engineering Science | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Solid Modeling, Drafting Software Usage |
| 21KSK19 | Communicative English | Humanities | 1 | English Grammar, Reading Comprehension, Public Speaking, Report Writing, Vocabulary Building |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT21 | Advanced Calculus and Numerical Methods | Basic Science | 4 | Laplace Transforms, Fourier Series, Partial Differential Equations, Numerical Methods, Finite Differences |
| 21CHE22 | Engineering Chemistry | Basic Science | 4 | Electrochemistry, Corrosion, Fuel Cells and Batteries, Polymers, Water Technology |
| 21EGD23 | Computer Aided Machine Drawing | Engineering Science | 3 | Orthographic Projections, Sectional Views, Assembly Drawings, Production Drawings, CAD Software for Machine Drawing |
| 21ELE24 | Basic Electrical Engineering | Engineering Science | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines |
| 21CPC25 | Object Oriented Programming with C++ | Engineering Science | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism and Virtual Functions, Exception Handling and Templates |
| 21CHE26 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, pH Metry, Conductometry, Viscosity Determination |
| 21ELE27 | Basic Electrical Engineering Lab | Lab | 1 | Ohm''''s Law Verification, KVL/KCL Experiments, Series/Parallel Circuits, Three-Phase Circuits, Motor Characteristics |
| 21CPC28 | Object Oriented Programming with C++ Lab | Lab | 1 | C++ Programs for OOP, Classes, Objects, Constructors, Inheritance and Polymorphism, File Handling in C++ |
| 21KSK29 | Environmental Studies | Humanities | 1 | Ecosystems and Biodiversity, Environmental Pollution, Climate Change, Waste Management, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML31 | Data Structures and Algorithms | Professional Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Sorting Algorithms, Searching Algorithms |
| 21AIML32 | Discrete Mathematical Structures | Professional Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Trees and Boolean Algebra, Number Theory |
| 21AIML33 | Analog and Digital Electronics | Engineering Science | 3 | Operational Amplifiers, Digital Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Converters (ADC/DAC) |
| 21AIML34 | Computer Organization and Architecture | Professional Core | 3 | Basic Computer Organization, CPU Design and Functions, Memory Organization, Input/Output Organization, Pipelining and Parallel Processing |
| 21AIML35 | Python Programming | Professional Core | 3 | Python Language Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, File I/O and Exception Handling |
| 21AIML36 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linked Lists, Stacks and Queues using Arrays, Tree Traversal Algorithms, Sorting and Searching Algorithms |
| 21AIML37 | Analog and Digital Electronics Lab | Lab | 1 | Op-Amp Applications, Logic Gates and Their Characteristics, Combinational Circuits (Adders, Decoders), Sequential Circuits (Flip-Flops, Counters) |
| 21AIML38 | Python Programming Lab | Lab | 1 | Basic Python Programs, Programs for Data Structures, Object-Oriented Python Applications, File Operations in Python |
| 21CIP39 | Constitution of India, Professional Ethics & Cyber Law | Humanities | 1 | Indian Constitution, Fundamental Rights and Duties, Professional Ethics, Cyber Crime and IT Act, Corporate Governance |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML41 | Analysis and Design of Algorithms | Professional Core | 3 | Algorithm Analysis, Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, NP-Completeness |
| 21AIML42 | Operating Systems | Professional Core | 3 | Operating System Structures, Process Management, CPU Scheduling, Memory Management, File Systems |
| 21AIML43 | Database Management Systems | Professional Core | 3 | ER Model, Relational Model, Structured Query Language (SQL), Normalization, Transaction Management |
| 21AIML44 | Probability and Statistics | Basic Science | 3 | Probability Distributions, Hypothesis Testing, Regression Analysis, Correlation, ANOVA |
| 21AIML45 | Data Science | Professional Core | 3 | Introduction to Data Science, Data Preprocessing, Data Visualization, Introduction to Machine Learning, Big Data Concepts |
| 21AIML46 | Analysis and Design of Algorithms Lab | Lab | 1 | Implementation of Graph Algorithms, Dynamic Programming Problems, Sorting and Searching Algorithms, Backtracking Algorithms |
| 21AIML47 | Database Management Systems Lab | Lab | 1 | SQL Queries for Data Manipulation, Database Design, Triggers and Stored Procedures, NoSQL Database Basics |
| 21AIML48 | Data Science Lab | Lab | 1 | Data Cleaning and Transformation, Data Visualization using Python, Basic Machine Learning Models, Feature Engineering |
| 21UDH49 | Universal Human Values | Humanities | 1 | Self-exploration and Self-awareness, Understanding Harmony in Relationships, Understanding Harmony in Society, Understanding Harmony in Nature, Holistic Understanding for Professional Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML51 | Artificial Intelligence | Professional Core | 3 | Intelligent Agents, Problem Solving through Search, Knowledge Representation, Planning and Reasoning, Introduction to Machine Learning |
| 21AIML52 | Machine Learning | Professional Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods, Support Vector Machines |
| 21AIML53 | Web Technologies | Professional Core | 3 | HTML, CSS, JavaScript, Client-Server Architecture, Web Servers and Databases, XML and AJAX, Web Security Fundamentals |
| 21AIMLE541 | Professional Elective - I (e.g., Natural Language Processing) | Elective | 3 | Text Preprocessing, Language Models, Machine Translation, Sentiment Analysis, Named Entity Recognition |
| 21AIMLO551 | Open Elective - I (e.g., Introduction to Data Analytics) | Elective | 3 | Data Analytics Process, Descriptive Statistics, Data Mining Techniques, Predictive Modeling Basics, Business Intelligence Tools |
| 21AIML56 | Artificial Intelligence Lab | Lab | 1 | Implementing Search Algorithms (BFS, DFS), AI Game Playing Agents, Knowledge Representation Systems, Expert Systems Development |
| 21AIML57 | Machine Learning Lab | Lab | 1 | Implementation of Regression Models, Classification Algorithms (Decision Trees, SVM), Clustering Algorithms (K-Means), Model Evaluation Metrics |
| 21AIML58 | Internship/Mini-Project | Project/Internship | 2 | Problem Definition, Literature Survey, Design and Implementation, Testing and Debugging, Report Writing and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML61 | Deep Learning | Professional Core | 3 | Neural Networks Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| 21AIML62 | Big Data Analytics | Professional Core | 3 | Hadoop Ecosystem, MapReduce Programming, HDFS, Spark Architecture, NoSQL Databases |
| 21AIML63 | Reinforcement Learning | Professional Core | 3 | Markov Decision Processes, Value Iteration and Policy Iteration, Q-Learning and SARSA, Deep Reinforcement Learning, Exploration-Exploitation Dilemma |
| 21AIMLE641 | Professional Elective - II (e.g., Computer Vision) | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Recognition, Image Segmentation, Deep Learning for Vision |
| 21AIMLO651 | Open Elective - II (e.g., Business Analytics) | Elective | 3 | Data-driven Decision Making, Forecasting Models, Optimization Techniques, Prescriptive Analytics, Case Studies in Business Analytics |
| 21AIML66 | Deep Learning Lab | Lab | 1 | Implementing CNNs for Image Classification, RNNs for Sequence Data, Transfer Learning Techniques, Hyperparameter Tuning |
| 21AIML67 | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and HDFS Commands, MapReduce Programs, Spark RDD Operations, NoSQL Database Integration |
| 21AIML68 | Mini Project/Seminar | Project/Seminar | 2 | Literature Review on advanced topics, Problem Identification and Formulation, Feasibility Study, Implementation of a Mini Project, Technical Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML71 | Cryptography and Network Security | Professional Core | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hashing and Digital Signatures, Network Security Protocols (SSL/TLS), Firewalls and Intrusion Detection |
| 21AIML72 | Professional Practice, Project Work - I | Project | 3 | Project Proposal Formulation, Literature Survey and Problem Identification, Requirement Analysis, System Design and Architecture, Prototyping and Initial Implementation |
| 21AIMLE731 | Professional Elective - III (e.g., Computer Vision and Image Processing) | Elective | 3 | Image Enhancement and Restoration, Feature Detection and Extraction, Object Detection Algorithms, Image Segmentation, Deep Learning for Image Analysis |
| 21AIMLE741 | Professional Elective - IV (e.g., Robotics and Automation) | Elective | 3 | Robot Kinematics and Dynamics, Path Planning Algorithms, Sensors and Actuators in Robotics, Industrial Robotics Applications, Human-Robot Interaction |
| 21AIMLO751 | Open Elective - III (e.g., Entrepreneurship and Startups) | Elective | 3 | Business Models and Plan Development, Market Analysis and Strategy, Startup Funding and Legal Aspects, Innovation and Idea Generation, Intellectual Property Rights |
| 21AIML76 | Advanced Machine Learning Lab (Minor Project) | Lab/Project | 2 | Implementation of Complex ML/DL Models, Research-oriented Problem Solving, Experimentation and Performance Evaluation, Technical Report Writing, Presentation of Results |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIMLE811 | Professional Elective - V (e.g., Edge AI) | Elective | 3 | Introduction to IoT Devices, Embedded AI Architectures, Model Optimization for Edge Devices, Privacy and Security in Edge AI, Applications of Edge AI |
| 21AIML82 | Project Work - II (Major Project) | Project | 6 | Comprehensive Project Development, Module Integration and Testing, Deployment Strategies, Project Management and Documentation, Final Presentation and Viva-Voce |
| 21AIML83 | Internship (Mandatory) | Internship | 6 | Real-world Industry Exposure, Application of AI/ML Skills, Professional Communication and Teamwork, Problem-solving in Industrial Context, Internship Report and Presentation |
| 21AIML84 | Seminar/Technical Talk | Seminar | 1 | Research Topic Selection, Literature Review, Presentation Skills, Critical Analysis, Q&A Session |
