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B-E in Artificial Intelligence Machine Learning 120 Seats at Alva's Institute of Engineering and Technology
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning (120 seats) at Alva's Institute of Engineering and Technology Dakshina Kannada?
This Artificial Intelligence & Machine Learning program at Alva''''s Institute of Engineering and Technology focuses on equipping students with expertise in intelligent systems, data science, and advanced algorithms. With India''''s rapidly expanding tech landscape and increasing adoption of AI in sectors like healthcare, finance, and e-commerce, this specialization addresses the critical demand for skilled AI/ML professionals. The program differentiates itself by providing a strong theoretical foundation coupled with extensive practical exposure, preparing students for innovative roles in the Indian industry.
Who Should Apply?
This program is ideal for fresh graduates who possess a strong aptitude for mathematics, programming, and problem-solving, seeking entry into high-growth tech domains. It also caters to aspiring researchers interested in contributing to AI advancements. Students with a keen interest in data analysis, algorithm design, and creating intelligent solutions for real-world challenges will thrive in this specialization, leveraging their analytical skills to shape India''''s digital future.
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 Robotics Programmer, with starting salaries ranging from INR 4-8 LPA for freshers, growing significantly with experience. The program aligns with industry demands for certified professionals, fostering growth trajectories in top Indian IT firms and startups. Graduates will be prepared to innovate and lead in the AI-driven transformation across various sectors.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Mathematical Concepts- (Semester 1-2)
Dedicate time in the initial semesters to build an unshakeable foundation in C, Python, Data Structures, and core mathematical subjects like Linear Algebra and Calculus. Regularly solve coding challenges on platforms to reinforce algorithmic thinking and mathematical problem-solving skills, which are critical for advanced AI/ML concepts.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy for Math, NPTEL courses for core CS
Career Connection
Strong fundamentals are the bedrock for understanding complex AI/ML algorithms and securing entry-level developer or analyst roles during campus placements.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss challenging topics, explain concepts to each other, and work on small programming projects together. Participate in college-level coding clubs or hackathons to apply theoretical knowledge in a collaborative environment and learn from diverse perspectives.
Tools & Resources
GitHub for project collaboration, Discord/WhatsApp for group discussions, College coding clubs
Career Connection
Develops teamwork and communication skills, highly valued by Indian tech companies, and builds a strong professional network for future opportunities.
Cultivate Effective Study Habits and Time Management- (Semester 1-2)
Implement a structured study routine, prioritize subjects, and avoid last-minute cramming. Focus on understanding concepts rather than rote memorization. Regularly review previous topics and utilize college library resources for deeper learning and academic excellence.
Tools & Resources
Pomodoro Technique, Google Calendar for scheduling, College library, NPTEL videos
Career Connection
Ensures consistent academic performance, builds discipline, and prepares students for the rigorous demands of higher-level engineering studies and professional life.
Intermediate Stage
Build Practical AI/ML Projects and Participate in Competitions- (Semester 3-5)
Translate theoretical knowledge from Machine Learning, AI, and Deep Learning courses into practical projects. Focus on developing real-world applications using Python libraries like Scikit-learn, TensorFlow, and PyTorch. Actively participate in online AI/ML competitions on platforms like Kaggle or Hackerearth to gain hands-on experience and build a portfolio.
Tools & Resources
Kaggle, Hackerearth, Colab/Jupyter Notebooks, Scikit-learn, TensorFlow, PyTorch
Career Connection
A strong project portfolio and competition wins significantly enhance internship and placement prospects, demonstrating applied skills to Indian tech recruiters.
Seek Early Industry Exposure through Internships and Workshops- (Semester 3-5)
Actively look for short-term internships, virtual internships, or industry-led workshops focusing on AI/ML. Even a two-month internship can provide invaluable insights into industry practices, tools, and challenges. Attend technical talks by industry experts organised by the college or local professional bodies.
Tools & Resources
Internshala, LinkedIn, College placement cell, IEEE/ACM student chapters
Career Connection
Gains practical industry experience, builds a professional network, and makes students more ''''job-ready'''' for core AI/ML roles in Indian companies.
Develop Specialised Skills in Data Handling and Big Data- (Semester 3-5)
Beyond core ML, dive deeper into data-related technologies, focusing on efficient data collection, cleaning, and storage. Explore Big Data concepts and tools like Hadoop and Spark, which are crucial for large-scale AI applications. Learn SQL and NoSQL databases comprehensively.
Tools & Resources
SQL Practice platforms, Apache Hadoop tutorials, Spark documentation, Coursera/edX courses on Big Data
Career Connection
Equips students for roles as Data Engineers or Big Data Analysts, which are in high demand in India''''s data-driven economy, and complements AI/ML skills.
Advanced Stage
Undertake a Capstone Project with Industry Relevance- (Semester 6-8)
In the final years, collaborate with faculty or industry mentors on a significant capstone project that addresses a real-world problem using advanced AI/ML techniques. Focus on demonstrating end-to-end problem-solving, from data acquisition and model development to deployment and evaluation. Aim for a publishable outcome or a prototype that can be showcased.
Tools & Resources
Research papers, Academic databases, Industry mentors, Cloud platforms (AWS/Azure/GCP)
Career Connection
Provides a flagship piece for resumes, impresses potential employers, and prepares students for research or product development roles in leading AI companies.
Intensive Placement Preparation and Soft Skill Development- (Semester 6-8)
Engage in focused interview preparation, including technical interviews (coding, algorithms, AI/ML concepts) and HR interviews (communication, problem-solving, behavioral). Participate in mock interviews conducted by the placement cell and refine soft skills like presentation, negotiation, and teamwork.
Tools & Resources
InterviewBit, Glassdoor for company-specific interview questions, College placement and training cell
Career Connection
Crucial for converting interview opportunities into successful placements with top-tier companies in India and for long-term career growth.
Explore Advanced Electives and Specialised Certifications- (Semester 6-8)
Choose professional electives wisely to deepen expertise in areas like Reinforcement Learning, NLP, Computer Vision, or AI for specific domains (e.g., healthcare, finance). Consider pursuing industry-recognized certifications from platforms like AWS, Google, or NVIDIA, demonstrating specialized skills highly valued in the Indian job market.
Tools & Resources
Official certification guides (AWS Certified Machine Learning, Google Cloud ML Engineer), Specialized online courses
Career Connection
Differentiates candidates for niche roles, demonstrates commitment to continuous learning, and opens doors to advanced career opportunities and higher salary packages.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Mathematics as compulsory subjects along with Chemistry/Biotechnology/Biology/Electronics/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies/Entrepreneurship as optional subjects. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together. Karnataka CET/COMEDK UGET qualified.
Duration: 8 semesters / 4 years
Credits: 135 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSL101 | Physics for Computer Science and Engineering | Core | 3 | Quantum Mechanics, Lasers and Optical Fibers, Electrical Properties of Materials, Magnetic Properties of Materials, Superconductors, Semiconductor Physics |
| MAL101 | Calculus and Differential Equations | Core | 3 | Differential Equations, Partial Differential Equations, Linear Algebra, Multivariable Calculus, Applications of Differential Equations |
| CSL101 | Programming for Problem Solving | Core | 3 | Introduction to C Programming, Control Flow Statements, Functions and Arrays, Pointers and Structures, File Handling, Searching and Sorting |
| EEL101 | Basic Electronics Engineering | Core | 3 | Diode Circuits, Transistor Biasing, Operational Amplifiers, Digital Logic Gates, Flip-flops, Microcontrollers |
| HSK101 | Communicative English | Core | 1 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Listening Skills, Presentation Skills |
| BSP101 | Physics for Computer Science and Engineering Lab | Lab | 1 | Optical Fiber Characteristics, Semiconductor Device Studies, Magnetic Field Measurement, Resistivity and Band Gap, Transistor Characteristics |
| CSP101 | Programming for Problem Solving Lab | Lab | 1 | C Program Structure, Conditional Statements, Looping Constructs, Functions and Pointers, Array and String Operations, File Input/Output |
| EEP101 | Basic Electronics Engineering Lab | Lab | 1 | Diode Rectifier Circuits, Transistor Amplifier Circuits, Op-Amp Applications, Logic Gate Realization, Flip-Flop Operations |
| MEL102 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Computer-Aided Drafting |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSL201 | Chemistry for Computer Science and Engineering | Core | 3 | Electrochemistry, Corrosion and its Control, Polymers and Composites, Energy Storage Devices, Water Technology, Nanomaterials |
| MAL201 | Vector Calculus and Linear Algebra | Core | 3 | Vector Differentiation, Vector Integration, Green''''s and Stokes'''' Theorem, Linear Transformations, Eigenvalues and Eigenvectors, Numerical Methods |
| CEL201 | Engineering Mechanics | Core | 3 | Forces and Moments, Equilibrium of Rigid Bodies, Friction, Centroid and Moment of Inertia, Work-Energy Principle, Kinematics of Particles |
| CVL201 | Elements of Civil Engineering | Core | 3 | Building Materials, Surveying, Transportation Engineering, Environmental Engineering, Water Resources Engineering, Structural Elements |
| HSK201 | Indian Constitution and Professional Ethics | Core | 1 | Preamble and Fundamental Rights, Directive Principles of State Policy, Parliamentary System, Judiciary, Engineering Ethics, Cyber Law |
| BSP201 | Chemistry for Computer Science and Engineering Lab | Lab | 1 | Potentiometric Titration, Conductometric Titration, Viscosity Measurement, pH Determination, Colorimetric Analysis |
| CSP201 | C Programming Lab | Lab | 1 | Arrays and Matrices, Structures and Unions, Pointers and Dynamic Memory Allocation, Function Pointers, File Operations |
| MEL202 | Workshop Practice | Lab | 1 | Fitting and Carpentry, Welding and Soldering, Sheet Metal Operations, Foundry Practices, Machine Tools |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BML301 | Linear Algebra and Computational Statistics | Core | 3 | Vector Spaces, Inner Product Spaces, Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA |
| CSL302 | Data Structures and Applications | Core | 3 | Arrays and Pointers, Linked Lists, Stacks and Queues, Trees and Graphs, Hashing Techniques, Sorting and Searching Algorithms |
| ECL303 | Analog and Digital Electronics | Core | 3 | Operational Amplifiers, Analog-to-Digital Conversion, Boolean Algebra, Combinational Logic Circuits, Sequential Logic Circuits, Memory Devices |
| CSL304 | Computer Organization and Architecture | Core | 3 | Basic Structure of Computers, Machine Instructions and Programs, Input/Output Organization, Memory System, Arithmetic Operations, Pipelining |
| CSL305 | Python Programming | Core | 3 | Python Basics and Data Types, Control Flow and Functions, Object-Oriented Programming, Modules and Packages, File I/O, Data Manipulation with Pandas |
| CSP306 | Data Structures Lab | Lab | 1 | Linked List Implementation, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Hashing Techniques, Sorting Algorithms |
| ECP307 | Analog and Digital Electronics Lab | Lab | 1 | Op-Amp Characteristics, Adder/Subtractor Circuits, Multiplexers/Demultiplexers, Flip-Flop Implementations, Counters and Registers |
| CSP308 | Python Programming Lab | Lab | 1 | Basic Python Programs, Functions and Modules, Object-Oriented Concepts, Exception Handling, File Operations, Data Analysis with Libraries |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSL401 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, Backtracking and Branch & Bound |
| CSL402 | Operating Systems | Core | 3 | Operating System Structures, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Systems |
| CSL403 | Database Management Systems | Core | 3 | Introduction to DBMS, Entity-Relationship Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| BML404 | Discrete Mathematics and Graph Theory | Core | 3 | Set Theory, Logic and Proofs, Counting Techniques, Relations and Functions, Graph Theory, Trees and Connectivity |
| CSL405 | Java Programming | Core | 3 | Introduction to Java, Object-Oriented Programming in Java, Inheritance and Polymorphism, Exception Handling, Multithreading, GUI Programming with Swing/JavaFX |
| CSP406 | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, SQL Queries (Joins, Subqueries), Stored Procedures, Triggers and Views, Database Design |
| CSP407 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management, CPU Scheduling Algorithms, Inter-Process Communication, Memory Allocation Strategies |
| CSP408 | Java Programming Lab | Lab | 1 | Classes and Objects, Inheritance and Interfaces, Exception Handling Programs, Multithreaded Applications, GUI Applications |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML501 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Ensemble Methods, Model Evaluation and Validation, Feature Engineering |
| AIML502 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| AIML503 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving by Searching, Knowledge Representation, Logical Agents, Planning, Expert Systems |
| AIML504X | Professional Elective - 1 | Elective | 3 | Topics depend on chosen elective, e.g., Full Stack Development, Computer Graphics, Advanced Data Structures, Image Processing |
| AIML505X | Open Elective - 1 | Elective | 3 | Topics depend on chosen elective from other engineering/science disciplines |
| AIML506 | Machine Learning Lab | Lab | 1 | Data Preprocessing, Linear Regression Implementation, Classification Algorithms (SVM, Decision Trees), Clustering (K-Means), Model Evaluation Metrics |
| AIML507 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, TCP/UDP Protocol Implementation, Routing Protocols Simulation, Packet Analysis |
| AIML508 | Artificial Intelligence Lab | Lab | 1 | Uninformed Search Algorithms, Informed Search Algorithms, Constraint Satisfaction Problems, Logic Programming (Prolog), Minimax Algorithm |
| AIML509 | Mini Project / Internship | Project/Internship | 1 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML601 | Deep Learning | Core | 3 | Neural Network Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow, PyTorch), Applications in Image and Text |
| AIML602 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Code Generation |
| AIML603 | Web Technologies | Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, Client-Side Scripting, Server-Side Technologies (Node.js/Python/PHP), Database Connectivity, Web Security Basics |
| AIML604X | Professional Elective - 2 | Elective | 3 | Topics depend on chosen elective, e.g., Cryptography and Network Security, Cloud Computing, Data Mining, Mobile Application Development |
| AIML605X | Open Elective - 2 | Elective | 3 | Topics depend on chosen elective from other engineering/science disciplines |
| AIML606 | Deep Learning Lab | Lab | 1 | Implement Feedforward Networks, CNN for Image Classification, RNN for Sequence Data, Transfer Learning, Hyperparameter Tuning |
| AIML607 | Web Technologies Lab | Lab | 1 | Front-end Development (HTML, CSS, JS), Responsive Web Design, Server-side Scripting, Database Integration, API Usage |
| AIML608 | Internship | Internship | 1 | Industry Exposure, Project Implementation, Teamwork and Communication, Problem-Solving in Real-World Context, Professional Report Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML701 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Stream Processing, Big Data Visualization |
| AIML702 | Natural Language Processing | Core | 3 | Language Models, Text Preprocessing, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Machine Translation |
| AIML703X | Professional Elective - 3 | Elective | 3 | Topics depend on chosen elective, e.g., Reinforcement Learning, Robotics and Automation, Cyber Security, Computer Vision |
| AIML704X | Professional Elective - 4 | Elective | 3 | Topics depend on chosen elective, e.g., Quantum Computing, Game Theory, Cognitive Science, AI for Healthcare |
| AIML705 | Project Work - Phase 1 | Project | 3 | Project Proposal, Detailed Literature Review, System Design and Architecture, Initial Implementation, Progress Report and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AIML801X | Professional Elective - 5 | Elective | 3 | Topics depend on chosen elective, e.g., Ethical Hacking, Digital Forensics, Augmented Reality / Virtual Reality, Business Intelligence |
| AIML802 | Internship / Project Work - Phase 2 | Project/Internship | 10 | Full System Implementation, Testing and Validation, Performance Analysis, Comprehensive Documentation, Final Presentation and Viva-Voce |
