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B-E in Artificial Intelligence Engineering 60 Seats at Alva's Institute of Engineering and Technology
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence & Engineering (60 seats) at Alva's Institute of Engineering and Technology Dakshina Kannada?
This Artificial Intelligence & Machine Learning Engineering program at Alva''''s Institute of Engineering and Technology focuses on equipping students with advanced skills in AI, ML, and data science. Designed to address the rapidly growing demand for AI professionals in India, the program emphasizes practical application and theoretical foundations. It prepares graduates for diverse roles in data-driven industries, aligning with the country''''s push for technological innovation.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into the dynamic field of artificial intelligence. It also suits engineering graduates or working professionals aspiring to transition into AI/ML roles or upskill with the latest industry-relevant technologies. Candidates with a foundational understanding of programming and data structures will find the curriculum stimulating and beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as AI engineers, data scientists, machine learning engineers, and research analysts in India''''s booming tech sector. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals earning significantly more. The program fosters growth trajectories in leading Indian and multinational companies, aligning with certifications in cloud AI platforms and data analytics.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice problem-solving using C and Python. Focus on understanding data types, control flow, functions, and basic algorithms. Participate in coding challenges.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Programming
Career Connection
Strong programming foundations are crucial for all AI/ML roles, serving as the bedrock for implementing complex algorithms and data processing.
Build a Strong Math Base- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics. Use online resources and textbooks to deepen understanding beyond classroom lectures. Form study groups for peer learning.
Tools & Resources
Khan Academy, NPTEL (Applied Mathematics), 3Blue1Brown (YouTube), textbooks by Gilbert Strang, Sheldon Ross
Career Connection
A robust mathematical background is essential for comprehending AI/ML algorithms, model optimization, and data interpretation, giving a competitive edge.
Engage in Early Project Exploration- (Semester 1-2)
Start with small, personal projects. Implement simple data structures or basic algorithms learned in labs. Participate in college tech events and coding competitions to apply theoretical knowledge.
Tools & Resources
GitHub for version control, basic Python IDEs, college hackathons
Career Connection
Early project experience enhances practical skills and helps build a foundational portfolio, making resumes stand out during internships and initial placements.
Intermediate Stage
Specialize in Core AI/ML Concepts- (Semester 3-5)
Dive deep into Data Structures, Algorithms, Object-Oriented Programming, and Machine Learning. Implement algorithms from scratch and use libraries like Scikit-learn, Pandas, NumPy.
Tools & Resources
Kaggle for datasets and competitions, Coursera/Udemy courses (Andrew Ng''''s ML course), Jupyter Notebooks, TensorFlow/PyTorch tutorials
Career Connection
Specialization in core AI/ML makes students highly desirable for roles like ML Engineer, Data Scientist, requiring hands-on experience with algorithm implementation and model building.
Seek Internships and Industry Exposure- (Semester 4-5)
Actively search for summer internships or part-time roles in AI/ML startups or tech companies. Attend industry workshops, webinars, and guest lectures to understand current trends.
Tools & Resources
LinkedIn, Internshala, college placement cell, industry meetups
Career Connection
Internships provide invaluable real-world experience, networking opportunities, and often lead to pre-placement offers, accelerating career entry into AI/ML.
Build a Strong Project Portfolio- (Semester 3-5)
Develop substantial projects applying ML/DL concepts to real-world problems. Document projects thoroughly on GitHub, focusing on problem statement, data, methodology, and results.
Tools & Resources
GitHub, personal website/blog, Google Colab, cloud platforms (AWS/GCP free tiers)
Career Connection
A robust project portfolio demonstrates practical skills and problem-solving abilities to recruiters, significantly boosting placement prospects.
Advanced Stage
Engage in Advanced AI/ML Research/Projects- (Semester 6-7)
Undertake major projects in Deep Learning, Computer Vision, NLP, or Reinforcement Learning. Explore research papers, contribute to open-source projects, or work with faculty on research.
Tools & Resources
ArXiv, IEEE Xplore, Google Scholar, advanced deep learning frameworks
Career Connection
Advanced projects and research experience are critical for roles in R&D, specialized AI engineering, or pursuing higher studies (M.Tech/Ph.D.) in AI.
Focus on Placement and Interview Preparation- (Semester 7-8)
Practice technical interview questions, revise core computer science and AI/ML concepts. Work on communication and soft skills. Attend mock interviews and career counseling sessions.
Tools & Resources
LeetCode (interview prep), Glassdoor (company interviews), resume builders, campus placement drives
Career Connection
Thorough preparation increases the likelihood of securing top placements in leading tech companies, ensuring a strong start to a professional career.
Network and Professional Development- (Semester 6-8)
Attend industry conferences, connect with professionals on LinkedIn, and participate in professional AI/ML communities. Consider leadership roles in student tech clubs.
Tools & Resources
LinkedIn, professional bodies like IEEE/ACM, local AI/ML meetups
Career Connection
Networking opens doors to job opportunities, mentorship, and keeps students updated on industry trends, which is vital for long-term career growth in the fast-evolving AI field.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MA11 | Calculus and Differential Equations | Core | 3 | Differential Calculus, Integral Calculus, Differential Equations, Vector Calculus, Laplace Transforms |
| 21CH12 | Engineering Chemistry | Core | 3 | Electrochemical Cells, Corrosion and its Control, Energy Storage Devices, Fuels and Combustion, Polymers and Engineering Materials |
| 21PSP13 | Programming for Problem Solving | Core | 3 | Introduction to C Programming, Control Structures, Functions and Pointers, Arrays and Strings, Structures and File Handling |
| 21EGD14 | Engineering Graphics | Core | 2 | Orthographic Projections, Projections of Solids, Sectioning of Solids, Development of Surfaces, Isometric Projections |
| 21ELN15 | Basic Electronics | Core | 3 | Diode Circuits, Transistor Biasing, Amplifiers, Digital Electronics Fundamentals, Operational Amplifiers |
| 21CIV16 | Elements of Civil Engineering & Mechanics | Core | 3 | Building Materials, Surveying and Geomatics, Transportation Engineering, Water Resources, Engineering Mechanics |
| 21CPL17 | Computer Programming Laboratory | Lab | 1 | C Program Debugging, Conditional Statements, Loops and Arrays, Functions and Strings, Pointers and Structures |
| 21CHL18 | Engineering Chemistry Laboratory | Lab | 1 | Volumetric Analysis, Instrumental Methods, Water Quality Analysis, Polymer Synthesis, Surface Tension Measurement |
| 21KSK19 | Communicative English & Kannada | Core | 1 | Grammar and Vocabulary, Listening and Speaking Skills, Reading Comprehension, Writing Skills, Interpersonal Communication |
| 21CIP110 | Constitution of India & Professional Ethics | Core | 1 | Indian Constitution Overview, Fundamental Rights and Duties, Parliamentary System, Professional Ethics, Moral Values and Responsibility |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MA21 | Advanced Calculus and Numerical Methods | Core | 3 | Partial Differential Equations, Fourier Series, Numerical Solution of ODEs, Finite Differences, Interpolation and Curve Fitting |
| 21PH22 | Engineering Physics | Core | 3 | Quantum Mechanics, Lasers and Holography, Optical Fibers, Superconductivity, Nanomaterials |
| 21EEE23 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Transformers, DC Machines, AC Machines |
| 21CPL24 | Programming for Problem Solving | Core | 3 | Advanced C Concepts, Problem-Solving Methodologies, Algorithmic Thinking, Debugging Techniques, Introduction to Data Structures |
| 21ME25 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, Power Plants, IC Engines, Refrigeration and Air Conditioning, Machine Tools |
| 21CIV26 | Environmental Studies | Core | 1 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Climate Change, Sustainable Development |
| 21PHL27 | Engineering Physics Laboratory | Lab | 1 | Spectroscopy, Optical Experiments, Semiconductor Devices, Magnetic Field Measurements, Material Characterization |
| 21CPL28 | Computer Aided Engineering Drawing | Lab | 2 | CAD Software Basics, 2D Drawing Commands, 3D Modeling, Assembly Drawing, Dimensioning and Tolerancing |
| 21KSK29 | Communicative English & Kannada | Core | 1 | Advanced Communication, Technical Writing, Presentation Skills, Group Discussions, Professional Etiquette |
| 21LCS210 | Life Skills for Engineers | Core | 1 | Self-Awareness, Teamwork and Leadership, Time Management, Stress Management, Goal Setting |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM31 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching Algorithms, Hashing |
| 21AIM32 | Object Oriented Programming with Java | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, Multithreading and Collections |
| 21AIM33 | Discrete Mathematics | Core | 3 | Propositional Logic, Set Theory and Relations, Functions and Sequences, Combinatorics and Probability, Graph Theory |
| 21AIM34 | Computer Organization and Architecture | Core | 3 | Basic Computer Functions, CPU Organization, Memory System Design, Input/Output Organization, Pipelining and Parallel Processing |
| 21AIM35 | Data Communication and Networking | Core | 3 | Network Models (OSI, TCP/IP), Physical Layer Concepts, Data Link Layer Protocols, Network Layer Services, Transport Layer Protocols |
| 21AIM36 | Data Structures and Algorithms Laboratory | Lab | 1 | Linked List Implementation, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| 21AIM37 | Object Oriented Programming with Java Laboratory | Lab | 1 | Java Class Design, Inheritance Examples, Polymorphism Applications, Exception Handling Practice, File I/O in Java |
| 21AIM38 | Mini Project (AIM) | Project | 1 | Problem Identification, System Design, Implementation and Testing, Project Documentation, Presentation Skills |
| 21AIK39 | AI/ML Skill Lab | Skill Lab | 1 | Python for Data Science, NumPy and Pandas Basics, Data Visualization with Matplotlib, Introduction to Scikit-learn, Basic Machine Learning Models |
| 21AIM310 | Applied Mathematics for AI&ML | Core | 3 | Linear Algebra, Probability Theory, Statistics for Data Analysis, Optimization Techniques, Multivariate Calculus |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM41 | Design and Analysis of Algorithms | Core | 3 | Algorithmic Paradigms (Divide & Conquer, Greedy), Dynamic Programming, Graph Algorithms, Complexity Classes (P, NP, NP-Complete), Amortized Analysis |
| 21AIM42 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks and Concurrency |
| 21AIM43 | Database Management Systems | Core | 3 | ER Modeling, Relational Model and Algebra, SQL Querying, Normalization, Transaction Management |
| 21AIM44 | Artificial Intelligence | Core | 3 | Intelligent Agents, Search Algorithms (BFS, DFS, A*), Knowledge Representation, Uncertainty and Probabilistic Reasoning, Machine Learning Introduction |
| 21AIM45 | Web Technology | Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, Server-side Scripting, Web Security Basics, Introduction to Web Frameworks |
| 21AIM46 | Design and Analysis of Algorithms Laboratory | Lab | 1 | Implementation of Sorting Algorithms, Graph Traversal, Dynamic Programming Solutions, Greedy Algorithm Problems, Time Complexity Analysis |
| 21AIM47 | Database Management Systems Laboratory | Lab | 1 | DDL and DML Commands, Advanced SQL Queries, Database Design, Transactions and Views, Database Connectivity |
| 21AIM48 | Artificial Intelligence Laboratory | Lab | 1 | Search Algorithm Implementation, Constraint Satisfaction Problems, Logical Reasoning with Prolog, Heuristic Search Techniques, Basic Expert System Development |
| 21AIM49 | Skill Lab (Web Technology) | Skill Lab | 1 | Responsive Web Design, JavaScript DOM Manipulation, Frontend Frameworks (React/Angular/Vue), Backend Development with Node.js/Django, API Integration |
| 21AIM410 | Probability and Statistics for Engineers | Core | 3 | Probability Distributions, Random Variables, Sampling Theory, Hypothesis Testing, Regression and Correlation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM51 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Feature Engineering, Ensemble Methods |
| 21AIM52 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, Spark Framework, NoSQL Databases, Data Ingestion and Processing |
| 21AIM53 | Software Engineering | Core | 3 | Software Process Models, Requirements Engineering, Software Design, Software Testing, Project Management and Quality Assurance |
| 21AIM543 | Natural Language Processing | Professional Elective | 3 | Text Preprocessing, Language Models, Text Classification, Information Extraction, Machine Translation Fundamentals |
| 21AIM55X | Open Elective - 1 | Open Elective | 3 | |
| 21AIM56 | Machine Learning Laboratory | Lab | 1 | Linear Regression Implementation, Classification Algorithms (SVM, Decision Trees), Clustering (K-Means), Model Hyperparameter Tuning, Data Preprocessing Techniques |
| 21AIM57 | Big Data Analytics Laboratory | Lab | 1 | HDFS Operations, MapReduce Programming, Spark RDDs, Hive Queries, Cassandra Database Operations |
| 21AIM58 | Project Work Phase - I | Project | 1 | Project Proposal Development, Literature Survey, Requirements Analysis, Preliminary Design, Feasibility Study |
| 21AIM59 | Internship/Mini-Project | Internship/Project | 1 | Industry Exposure, Practical Skill Application, Teamwork, Problem-Solving in Real-World Scenarios, Report Writing |
| 21AIM510 | Research Methodology & Intellectual Property Rights | Core | 1 | Research Problem Formulation, Data Collection and Analysis, Report Writing and Presentation, Introduction to IPR, Patents, Copyrights, Trademarks |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM61 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow, PyTorch), Transfer Learning |
| 21AIM62 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization, Cloud Security, Cloud Platforms (AWS, Azure, GCP) |
| 21AIM63 | Computer Vision | Core | 3 | Image Processing Fundamentals, Feature Extraction and Matching, Object Detection and Recognition, Image Segmentation, Deep Learning for Computer Vision |
| 21AIM644 | Genetic Algorithms & Swarm Intelligence | Professional Elective | 3 | Evolutionary Computation, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Applications of Swarm Intelligence |
| 21AIM65X | Open Elective - 2 | Open Elective | 3 | |
| 21AIM66 | Deep Learning Laboratory | Lab | 1 | Implementing Neural Networks, CNNs for Image Classification, RNNs for Sequence Data, TensorFlow/Keras Practice, Hyperparameter Tuning |
| 21AIM67 | Cloud Computing Laboratory | Lab | 1 | AWS/Azure/GCP Console Navigation, Deploying Virtual Machines, Storage Services, Serverless Computing, Containerization (Docker) |
| 21AIM68 | Professional Practice (A Project Based Learning) | Project | 1 | Problem Identification and Scope, Iterative Development, Team Collaboration, Documentation and Reporting, Project Presentation |
| 21AIM69 | Interdisciplinary Project | Project | 1 | Cross-Disciplinary Problem Solving, Integration of Diverse Knowledge, Project Planning, Solution Implementation, Interdisciplinary Communication |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM71 | Artificial Neural Networks | Core | 3 | Perceptrons and Multi-layer Perceptrons, Backpropagation Algorithm, Radial Basis Function Networks, Self-Organizing Maps, Hopfield Networks |
| 21AIM72 | Advanced Machine Learning | Core | 3 | Bayesian Learning, Kernel Methods, Graphical Models, Causality in ML, Generative Models |
| 21AIM732 | Explainable AI | Professional Elective | 3 | Interpretability vs. Explainability, Local and Global Explanations, LIME, SHAP, and Grad-CAM, Fairness and Bias in AI, Responsible AI Development |
| 21AIM744 | Game Theory for AI | Professional Elective | 3 | Strategic Games, Nash Equilibrium, Extensive Games, Mechanism Design, Learning in Games |
| 21AIM75 | Project Work Phase - II | Project | 3 | Detailed System Design, Module-wise Implementation, Extensive Testing, Results Analysis, Interim Project Report |
| 21AIM76 | Internship/Industrial Training | Internship | 1 | Industry Best Practices, Real-World Problem Solving, Professional Networking, Skill Enhancement, Technical Report Submission |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIM81 | Major Project | Project | 7 | Final System Development, Performance Evaluation, Report Writing, Project Defense, Innovation and Scalability |
| 21AIM82 | Seminar | Core | 1 | Technical Literature Review, Presentation Skills, Topic Research, Audience Engagement, Q&A Handling |
| 21AIM83 | Technical Report Writing and Research Methodology | Core | 1 | Technical Writing Standards, Research Ethics, Paper Structure, Referencing and Citation, Scientific Communication |
| 21AIM84 | Internship | Internship | 10 | Full-time Industry Immersion, Advanced Skill Application, Corporate Environment Adaptability, Mentorship and Learning, Career Development |
