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B-TECH in Artificial Intelligence Machine Learning at Aditya Institute of Technology and Management
Srikakulam, Andhra Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Aditya Institute of Technology and Management Srikakulam?
This Artificial Intelligence & Machine Learning program at Aditya Institute of Technology and Management focuses on equipping students with advanced skills in designing and developing intelligent systems. It covers core AI concepts, machine learning algorithms, deep learning, and their practical applications, addressing the growing demand for AI/ML experts in the Indian technology sector and beyond. The program emphasizes hands-on experience and real-world problem-solving to foster innovation.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, programming, and logical reasoning, seeking entry into high-growth AI/ML roles. It also benefits working professionals looking to upskill in cutting-edge technologies and career changers aiming to transition into data science, machine learning engineering, or AI research, provided they have a foundational technical background.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as AI engineers, machine learning scientists, data scientists, and robotics engineers, with entry-level salaries typically ranging from INR 4-8 lakhs per annum, growing significantly with experience. The program prepares students for professional certifications in AI/ML and fosters an entrepreneurial mindset, enabling them to contribute to India''''s burgeoning tech ecosystem.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus rigorously on mastering C and Java programming, alongside data structures and algorithms. Utilize online coding platforms like HackerRank and LeetCode for daily practice, and understand theoretical concepts through problem-solving.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong foundational programming is crucial for all tech roles, especially in competitive coding rounds for placements at companies like TCS, Infosys, and Wipro.
Build a Strong Mathematical Base- (Semester 1-3)
Pay close attention to Linear Algebra, Calculus, Probability, and Statistics. These form the bedrock of AI and Machine Learning. Solve textbook problems, engage in peer study groups, and seek conceptual clarity from faculty.
Tools & Resources
Khan Academy, NPTEL Mathematics courses, NCERT Class 11/12 Math for revision
Career Connection
Essential for understanding underlying ML algorithms, model optimization, and research in AI, opening doors to advanced R&D roles.
Participate in Introductory Tech Workshops & Clubs- (Semester 1-2)
Join the college''''s coding clubs, AI/ML interest groups, or participate in introductory workshops. Attend sessions on Python basics, Git & GitHub, and introductory AI topics to gain exposure and network with seniors.
Tools & Resources
College tech clubs, GitHub, Codecademy, freeCodeCamp
Career Connection
Early exposure builds a portfolio, enhances soft skills, and helps identify areas of interest for future specialization, aiding in internship selections.
Intermediate Stage
Develop Practical AI/ML Projects- (Semester 3-5)
Beyond lab assignments, initiate small personal projects using Python, scikit-learn, and basic deep learning libraries. Focus on real-world datasets from platforms like Kaggle, applying learned ML/DL algorithms to solve practical problems.
Tools & Resources
Kaggle, Google Colab, scikit-learn, TensorFlow/Keras, Jupyter Notebooks
Career Connection
A strong project portfolio is vital for demonstrating practical skills to recruiters for roles like Data Scientist and ML Engineer, especially in Indian startups and product companies.
Seek Early Industry Exposure through Internships- (Summer after Semester 4 and Semester 6)
Actively search for internships during summer breaks (after 2nd and 3rd year). Even unpaid or short-term internships in startups can provide invaluable experience. Focus on roles related to data analysis, machine learning model development, or AI research.
Tools & Resources
Internshala, LinkedIn Jobs, College Placement Cell, Company Career Pages
Career Connection
Internships are a direct pathway to pre-placement offers (PPOs) and provide practical industry experience, making you highly employable.
Engage in Online Courses & Certifications- (Semester 4-6)
Supplement university curriculum with specialized online courses from platforms like Coursera, Udemy, or edX in areas like Deep Learning Specialization by Andrew Ng or Google''''s ML Crash Course. Obtain relevant certifications.
Tools & Resources
Coursera, Udemy, edX, NPTEL, IBM/Google certifications
Career Connection
Certifications enhance your resume, demonstrate proactive learning, and validate specialized skills, giving you an edge in competitive job markets across India.
Advanced Stage
Specialize and Contribute to Advanced Projects- (Semester 7-8)
Identify a niche within AI/ML (e.g., NLP, Computer Vision, Reinforcement Learning) and undertake a significant major project. Collaborate with faculty, participate in research, or contribute to open-source AI projects.
Tools & Resources
GitHub, Academic Research Papers, Specialized libraries (e.g., Hugging Face for NLP, OpenCV for Vision)
Career Connection
Deep specialization and impactful projects are key for securing roles in R&D, advanced ML engineering, or pursuing higher studies (M.Tech/Ph.D.).
Intensive Placement & Interview Preparation- (Semester 6-8)
Dedicate time to rigorous placement preparation, including mock interviews (technical and HR), aptitude tests, and revising core computer science concepts, especially Data Structures and Algorithms, System Design, and AI/ML fundamentals.
Tools & Resources
InterviewBit, LeetCode, Glassdoor, Company-specific interview experiences
Career Connection
Maximizes chances of securing high-paying placements with top tech companies and startups in India, ensuring a smooth transition from academics to career.
Develop Professional Networking & Communication Skills- (Semester 7-8)
Attend industry conferences, tech meetups, and alumni events. Practice presenting projects, articulate technical concepts clearly, and develop strong professional communication and negotiation skills for job interviews and future career growth.
Tools & Resources
LinkedIn, Industry events, Toastmasters/Public Speaking Clubs, College Alumni Network
Career Connection
Networking can lead to mentorship, job referrals, and insights into industry trends, while strong communication is crucial for leadership roles and client-facing positions.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies. Obtained at least 45% marks (40% for reserved category) in the above subjects taken together. Admissions through EAMCET counselling.
Duration: 4 years (8 semesters)
Credits: 150 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20BS1101 | Linear Algebra & Calculus | Basic Science | 3 | Matrices, Eigenvalues, Differential Calculus, Integral Calculus, Multivariable Calculus |
| R20BS1102 | Engineering Chemistry | Basic Science | 3 | Water Technology, Electrochemistry, Corrosion, Fuel Chemistry, Polymer Chemistry |
| R20HS1101 | English | Humanities and Social Sciences | 3 | Listening Skills, Speaking Skills, Reading Comprehension, Writing Skills, Grammar and Vocabulary |
| R20ES1101 | Programming for Problem Solving using C | Engineering Science | 3 | Introduction to C Programming, Data Types and Operators, Control Structures, Arrays and Strings, Functions and Pointers |
| R20ES1102 | Computer Engineering Workshop | Engineering Science | 3 | PC Hardware and Assembly, Networking Basics, Operating System Installation, Productivity Tools, Troubleshooting |
| R20BS1102L | Engineering Chemistry Lab | Basic Science Lab | 1.5 | Water Analysis, Volumetric Analysis, Electrochemistry Experiments, Viscosity and Surface Tension, pH and Conductivity Measurements |
| R20HS1101L | English Language Skills Lab | Humanities and Social Sciences Lab | 1.5 | Listening Comprehension, Pronunciation Practice, Group Discussions, Presentations, Interview Skills |
| R20ES1101L | Programming for Problem Solving using C Lab | Engineering Science Lab | 1.5 | C Program Development, Conditional Statements and Loops, Array and String Operations, Function and Pointer Implementation, File Input/Output |
| R20MC1101 | Environmental Science | Mandatory Course | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Global Environmental Issues, Environmental Protection |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20BS1201 | Differential Equations and Vector Calculus | Basic Science | 3 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Vector Differentiation, Vector Integration |
| R20BS1202 | Applied Physics | Basic Science | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Solid State Physics, Semiconductor Devices |
| R20ES1201 | Engineering Graphics & Design | Engineering Science | 3 | Engineering Curves, Orthographic Projections, Isometric Projections, Sections of Solids, Introduction to CAD |
| R20ES1202 | Data Structures | Engineering Science | 3 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Graphs, Sorting and Searching Algorithms |
| R20ES1203 | Object Oriented Programming through Java | Engineering Science | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling |
| R20BS1202L | Applied Physics Lab | Basic Science Lab | 1.5 | Laser Characteristics, Optical Fiber Communication, Photoelectric Effect, Energy Gap of Semiconductors, Magnetic Field Determination |
| R20ES1202L | Data Structures Lab | Engineering Science Lab | 1.5 | Stack and Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| R20ES1203L | Object Oriented Programming through Java Lab | Engineering Science Lab | 1.5 | Java Program Development, OOP Concepts Implementation, Inheritance and Polymorphism Practice, Exception Handling in Java, GUI Programming Basics |
| R20MC1201 | Indian Constitution | Mandatory Course | 0 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Governments, Indian Judiciary |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20BS2101 | Probability & Statistics | Basic Science | 3 | Probability Distributions, Sampling Theory, Hypothesis Testing, Correlation Analysis, Regression Analysis |
| R20PC2101 | Discrete Mathematics | Professional Core | 3 | Mathematical Logic, Set Theory and Relations, Functions and Combinatorics, Graph Theory, Recurrence Relations |
| R20PC2102 | Digital Logic Design | Professional Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters |
| R20PC2103 | Python Programming | Professional Core | 3 | Python Basics and Data Types, Control Flow and Functions, Data Structures in Python, Modules and Packages, Object-Oriented Programming |
| R20PC2104 | Database Management Systems | Professional Core | 3 | Database Concepts, ER Model, Relational Algebra, SQL Queries, Normalization and Transactions |
| R20PC2103L | Python Programming Lab | Professional Core Lab | 1.5 | Python Program Execution, Data Structure Implementation, Function and Module Usage, OOP in Python Practice, File Handling Exercises |
| R20PC2104L | Database Management Systems Lab | Professional Core Lab | 1.5 | SQL Query Practice, Database Design, PL/SQL Programming, Transaction Control, Data Manipulation |
| R20ES2101 | Data Visualization using Tableau/PowerBI | Engineering Science | 2 | Introduction to Tableau/PowerBI, Data Connection and Preparation, Chart Types and Dashboards, Interactive Reports, Data Storytelling |
| R20MC2101 | Essence of Indian Traditional Knowledge | Mandatory Course | 0 | Indian Literature and Arts, Indian Science and Technology, Indian Philosophical Systems, Indian Education System, Value System of India |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20BS2201 | Complex Analysis & Transforms | Basic Science | 3 | Complex Numbers and Functions, Analytic Functions, Complex Integration, Fourier Series and Transforms, Z-Transforms |
| R20PC2201 | Design and Analysis of Algorithms | Professional Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| R20PC2202 | Operating Systems | Professional Core | 3 | OS Concepts, Process Management, CPU Scheduling, Deadlocks, Memory Management |
| R20PC2203 | Artificial Intelligence | Professional Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Reasoning |
| R20PC2204 | Machine Learning | Professional Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Clustering Techniques |
| R20PC2201L | Design and Analysis of Algorithms Lab | Professional Core Lab | 1.5 | Sorting Algorithm Implementation, Searching Algorithm Practice, Graph Traversal Algorithms, Dynamic Programming Applications, Greedy Algorithm Solutions |
| R20PC2204L | Machine Learning Lab | Professional Core Lab | 1.5 | Data Preprocessing, Supervised Learning Models, Unsupervised Learning Models, Model Evaluation Metrics, Machine Learning Libraries |
| R20ES2201 | R Programming | Engineering Science | 2 | R Basics and Syntax, Data Structures in R, Data Manipulation, Data Visualization with R, Statistical Analysis in R |
| R20MC2201 | Professional Ethics & Human Values | Mandatory Course | 0 | Ethics in Engineering, Moral Values and Dilemmas, Professionalism and Codes of Ethics, Corporate Social Responsibility, Environmental Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20PC3101 | Theory of Computation | Professional Core | 3 | Finite Automata, Regular Expressions and Languages, Context-Free Grammars, Turing Machines, Undecidability |
| R20PC3102 | Computer Networks | Professional Core | 3 | Network Topologies, OSI and TCP/IP Models, Network Protocols (IP, TCP, UDP), Routing Algorithms, Network Security |
| R20PC3103 | Deep Learning | Professional Core | 3 | Neural Networks Fundamentals, Activation Functions and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs |
| R20PC3104 | Natural Language Processing | Professional Core | 3 | NLP Introduction, Text Preprocessing, Tokenization and POS Tagging, Named Entity Recognition, Sentiment Analysis |
| R20HS3101 | Professional Communication & Life Skills | Humanities and Social Sciences | 2 | Verbal and Non-verbal Communication, Interpersonal Skills, Time Management, Resume Writing and Interview Skills, Presentation Techniques |
| R20PC3103L | Deep Learning Lab | Professional Core Lab | 1.5 | Neural Network Implementation, CNNs for Image Classification, RNNs for Sequence Data, TensorFlow/Keras Usage, Model Training and Evaluation |
| R20PC3104L | Natural Language Processing Lab | Professional Core Lab | 1.5 | Text Data Preprocessing, NLTK/SpaCy Usage, Text Classification, Sentiment Analysis Implementation, Language Modeling |
| R20PR3101 | Minor Project-I / Internship | Project / Internship | 2 | Problem Definition, System Design, Implementation and Testing, Report Writing, Presentation Skills |
| R20MC3101 | Disaster Management | Mandatory Course | 0 | Types of Disasters, Disaster Management Cycle, Disaster Preparedness, Mitigation Strategies, Post-Disaster Recovery |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20PC3201 | Compiler Design | Professional Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| R20PC3202 | Big Data Analytics | Professional Core | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce Framework, HDFS, Spark and NoSQL Databases |
| R20PE320x | Professional Elective – I | Professional Elective | 3 | Computer Vision: Image Formation, Image Processing, Feature Extraction, Object Recognition, Deep Learning for Vision, Reinforcement Learning: Markov Decision Processes, Q-learning, Policy Gradients, Deep Reinforcement Learning, Applications, Ethics in AI: AI Bias, Fairness, Accountability, Transparency, Ethical AI Frameworks |
| R20OE320x | Open Elective – I | Open Elective | 3 | |
| R20PC3202L | Big Data Analytics Lab | Professional Core Lab | 1.5 | Hadoop Setup and Commands, MapReduce Programming, Hive and Pig Scripting, Spark Data Processing, NoSQL Database Operations |
| R20PE320xL | Professional Elective – I Lab | Professional Elective Lab | 1.5 | Computer Vision Lab: Image Processing with OpenCV, Object Detection, Reinforcement Learning Lab: Q-Learning Implementation, OpenAI Gym, Ethics in AI Lab: Case Studies, Bias Detection Tools |
| R20PR3201 | Minor Project-II / Internship | Project / Internship | 2 | Advanced Project Development, Literature Survey, System Architecture, Testing and Debugging, Technical Documentation |
| R20MC3201 | Universal Human Values 2 | Mandatory Course | 0 | Harmony in Self, Harmony in Family, Harmony in Society, Harmony in Nature/Existence, Holistic Understanding |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20PC4101 | Cloud Computing | Professional Core | 3 | Cloud Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technologies, Cloud Security |
| R20PC4102 | Data Warehousing and Data Mining | Professional Core | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Techniques, Association, Classification, Clustering |
| R20PE410x | Professional Elective – II | Professional Elective | 3 | Robotics Process Automation: RPA Concepts, Automation Tools, Bot Development, Process Mapping, Benefits of RPA, Image & Video Analytics: Image/Video Representation, Motion Analysis, Object Tracking, Event Recognition, Deep Learning for Video, Speech and Audio Processing: Speech Production, Acoustic Phonetics, Speech Recognition, Audio Feature Extraction, Speaker Identification |
| R20PE410x | Professional Elective – III | Professional Elective | 3 | Computer Organization & Architecture: CPU Design, Memory Hierarchy, I/O Organization, Pipelining, Instruction Set Architectures, High Performance Computing: Parallel Computing, Distributed Systems, Grid Computing, GPU Programming, HPC Architectures, Block Chain Technology: Cryptography, Distributed Ledger Technology, Blockchain Architecture, Consensus Mechanisms, Smart Contracts |
| R20OE410x | Open Elective – II | Open Elective | 3 | |
| R20PC4102L | Data Warehousing and Data Mining Lab | Professional Core Lab | 1.5 | Data Preprocessing Tools, OLAP Cube Operations, Association Rule Mining, Classification Algorithm Implementation, Clustering Algorithm Practice |
| R20PR4101 | Industrial Project / Internship | Project / Internship | 3 | Real-world Problem Solving, Industry Collaboration, Project Management Lifecycle, Innovation and Prototyping, Client Communication |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| R20PE420x | Professional Elective – IV | Professional Elective | 3 | Pattern Recognition: Statistical Pattern Recognition, Syntactic Pattern Recognition, Neural Networks for Pattern Recognition, Feature Selection, Classifier Design, Internet of Things: IoT Architecture, Sensors and Actuators, Communication Protocols, IoT Platforms, Data Analytics in IoT, Augmented Reality and Virtual Reality: AR/VR Devices, 3D Graphics, Interaction Techniques, Tracking, Applications of AR/VR |
| R20PE420x | Professional Elective – V | Professional Elective | 3 | Digital Image Processing: Image Fundamentals, Image Enhancement, Image Restoration, Image Compression, Image Segmentation, Predictive Analytics: Forecasting Techniques, Time Series Analysis, Machine Learning for Prediction, Model Deployment, A/B Testing, Computer Graphics: Graphics Primitives, 2D/3D Transformations, Viewing, Shading and Illumination, Ray Tracing |
| R20OE420x | Open Elective – III | Open Elective | 3 | |
| R20PR4201 | Major Project | Project | 8 | Large-scale Project Development, Research and Innovation, System Integration, Comprehensive Reporting, Final Presentation and Defense |
