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B-E in Artificial Intelligence Machine Learning at Acharya Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Acharya Institute of Technology Bengaluru?
This Artificial Intelligence & Machine Learning (AIML) program at Acharya Institute of Technology focuses on equipping students with a robust foundation in cutting-edge AI and ML technologies. It integrates theoretical concepts with practical applications, emphasizing problem-solving skills crucial for the rapidly evolving Indian tech industry. The program is designed to meet the growing demand for skilled professionals in areas like data science, intelligent systems, and automation. Its curriculum is updated to align with global trends and local industry needs.
Who Should Apply?
This program is ideal for aspiring engineers and innovators eager to delve into the world of intelligent systems and data-driven decision-making. Fresh graduates with a strong aptitude for mathematics and programming, seeking entry into high-growth tech roles, will find it highly beneficial. Working professionals looking to upskill in AI/ML, and career changers transitioning into the industry, especially those with an engineering or scientific background, are also well-suited for this comprehensive course.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths such as AI Engineer, Machine Learning Scientist, Data Scientist, Robotics Engineer, or NLP Specialist. Entry-level salaries in India typically range from INR 4-8 lakhs per annum, with experienced professionals commanding significantly higher packages (INR 12-30+ lakhs). The curriculum is designed to align with industry certifications and fosters growth trajectories in leading Indian and multinational technology companies, contributing to India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with C and Python- (Semester 1-2 (and ongoing))
Dedicate significant time to mastering programming logic and syntax in C (from Semester 1) and later Python (crucial for AI/ML). Practice problem-solving on platforms daily. Focus on understanding data structures and algorithms at a foundational level to build a strong coding base.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses
Career Connection
Strong programming skills are the bedrock for any AI/ML role, crucial for cracking technical interviews and efficiently implementing algorithms and models.
Build a Strong Mathematical & Statistical Base- (Semester 1-4)
Pay close attention to Engineering Mathematics I & II (Sem 1 & 2) and Probability & Statistics (Sem 4). These subjects provide the theoretical backbone for understanding complex AI/ML algorithms. Form study groups to clarify difficult concepts and solve numerous practice problems.
Tools & Resources
Khan Academy, MIT OpenCourseware, Textbooks on linear algebra, calculus, and probability, Online tutorial series
Career Connection
A solid mathematical foundation is essential for understanding algorithm mechanics, debugging model performance, and pursuing advanced research in artificial intelligence and machine learning.
Engage in Interdisciplinary Learning & Project-Based Exploration- (Semester 1-2)
Leverage the common engineering subjects in the first year to understand diverse engineering principles. Actively participate in mini-projects, even non-graded ones, to apply learned concepts. Join college technical clubs or hackathons to collaborate on ideas and build early prototypes, fostering innovation.
Tools & Resources
Tinkercad, Arduino, Raspberry Pi, Kaggle (for beginner datasets), GitHub
Career Connection
Develops problem-solving, teamwork, and practical application skills across various domains, making you a well-rounded and adaptable candidate for diverse tech roles in India.
Intermediate Stage
Develop a Portfolio of Practical AI/ML Projects- (Semester 3-5)
Beyond lab assignments, start building independent projects in AI/ML using Python and relevant libraries (TensorFlow, PyTorch, Scikit-learn). Focus on real-world datasets from platforms like Kaggle or UCI ML Repository. Document your code, articulate your methodology, and showcase it on GitHub.
Tools & Resources
Jupyter Notebooks, Google Colab, Kaggle, GitHub, Towards Data Science (for project ideas and tutorials)
Career Connection
A strong project portfolio is vital for demonstrating practical skills and problem-solving abilities to recruiters, significantly boosting your chances of securing internships and placements in AI/ML roles.
Seek Early Industry Exposure through Internships and Workshops- (Summer breaks after Semester 3 and Semester 4)
Actively look for short-term internships, virtual internships, or summer training programs in AI/ML-related fields after Semesters 3 or 4. Attend industry workshops, tech talks, and bootcamps organized by the college or external entities. Network actively with professionals on platforms like LinkedIn.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry-specific online forums and events
Career Connection
Gain practical experience, understand industry workflows, build professional networks, and identify potential career paths, making you job-ready for the Indian tech market.
Specialize through Electives and Advanced Learning- (Semester 5-6)
Carefully choose professional electives in Semesters 5 and 6 that align with your career interests (e.g., Computer Vision, NLP, Reinforcement Learning, Deep Learning). Supplement classroom learning with online advanced courses from Coursera, Udemy, or edX to gain deeper expertise in your chosen areas.
Tools & Resources
Coursera (e.g., Deep Learning Specialization by Andrew Ng), fast.ai, Udacity, Medium articles on specialized AI/ML topics
Career Connection
Develops specialized skills highly valued by employers, positioning you for specific and high-demand roles within the broader AI/ML domain, increasing your market value.
Advanced Stage
Intensive Placement Preparation & Mock Interviews- (Semester 6-8)
Begin rigorous preparation for placements from Semester 6. Focus on Data Structures and Algorithms, System Design, and advanced Machine Learning concepts. Participate in mock interviews (technical, HR, case studies) conducted by college career services or peer groups. Refine your resume and LinkedIn profile meticulously.
Tools & Resources
InterviewBit, LeetCode premium, Glassdoor (for company-specific interview questions), College placement cell resources and workshops
Career Connection
Maximizes chances of securing coveted placements with top-tier companies in India by honing interview skills and ensuring comprehensive technical knowledge required by recruiters.
Undertake a Capstone Project or Research Work- (Semester 7-8)
Invest significant effort into the Project Work Phase I & II (Sem 7 & 8) or any research project. Aim for an innovative solution to a complex, real-world problem, utilizing advanced AI/ML techniques. If possible, seek to publish your work in college journals or workshops, demonstrating research aptitude.
Tools & Resources
Research papers (e.g., arXiv), Academic conferences, Advanced ML frameworks, Cloud computing platforms (AWS, Azure, GCP), Overleaf for LaTeX documentation
Career Connection
Showcases exceptional problem-solving abilities, research aptitude, and advanced technical skills, highly valued for both industry R&D roles and pursuing higher studies or specialized roles.
Develop Soft Skills and Professional Ethics- (Throughout all semesters, with increased focus in Semesters 6-8)
Actively participate in workshops on communication, leadership, and teamwork. Understand ethical considerations in AI (from SECs and core subjects like Data Privacy) and apply them to your projects. Engage in professional bodies or student chapters to develop networking and leadership skills, preparing for corporate culture.
Tools & Resources
Toastmasters International (if available), College workshops on soft skills, AI Ethics guidelines from reputable organizations (e.g., NITI Aayog), Professional networking events
Career Connection
Essential for long-term career growth, leadership roles, and effective collaboration in a professional environment, differentiating you as a responsible and well-rounded professional in the Indian job market.
Program Structure and Curriculum
Eligibility:
- Candidates should have passed 10+2 / PUC with Physics and Mathematics as compulsory subjects along with Chemistry / Biology / Biotechnology / Computer Science / Electronics as optional subjects with English as one of the languages of study and obtained a minimum of 45% marks in aggregate in the optional subjects (40% for SC/ST/OBC candidates).
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 |
|---|---|---|---|---|
| 23MATS11 | Engineering Mathematics-I | Core | 3 | Differential Calculus, Integral Calculus, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| 23PCD12 | Programming in C and Data Structures | Core | 4 | C Fundamentals, Control Statements, Functions, Arrays and Pointers, Introduction to Data Structures |
| 23EGD13 | Engineering Graphics and Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, CAD Software Basics, Development of Surfaces |
| 23CHE14 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion and its Control, Engineering Materials, Water Technology, Fuels and Combustion |
| 23ELE15 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Three-Phase Systems, Electrical Machines, Electrical Safety |
| 23CHEL16 | Engineering Chemistry Laboratory | Lab | 1 | Volumetric Analysis, pH-metry Experiments, Conductometry, Colorimetry, Spectrophotometry |
| 23PCDL17 | C Programming and Data Structures Laboratory | Lab | 1 | Basic C Programs, Conditional Statements and Loops, Arrays and Strings Operations, Functions and Pointers, Simple Data Structures Implementation |
| 23EGH18 | English for Technical Communication | Core | 1 | Technical Report Writing, Effective Presentation Skills, Business Communication, Public Speaking, Grammar and Vocabulary for Engineers |
| 23CIV19 | Introduction to Civil Engineering | Audit | 0 | Civil Engineering Materials, Building Components, Surveying and Leveling, Transportation Engineering, Environmental Engineering Concepts |
| 23AI20 | Artificial Intelligence | Audit | 0 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MATS21 | Engineering Mathematics-II | Core | 3 | Linear Algebra, Multiple Integrals, Vector Integration, Numerical Methods, Complex Analysis |
| 23PHYL22 | Engineering Physics | Core | 4 | Quantum Mechanics, Lasers and Holography, Optical Fibers and their Applications, Superconductivity, Nanotechnology |
| 23EVE23 | Environmental Studies | Core | 1 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Renewable Energy Sources, Sustainable Development |
| 23EME24 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics Basics, IC Engines, Power Transmission Systems, Manufacturing Processes, Robotics Principles |
| 23BEE25 | Basic Electronics Engineering | Core | 4 | Semiconductor Diodes, Transistors and Amplifiers, Rectifiers and Filters, Digital Logic Gates, Operational Amplifiers |
| 23PHYL26 | Engineering Physics Laboratory | Lab | 1 | Laser Wavelength Measurement, Optical Fiber Characteristics, Hall Effect Experiment, Dielectric Constant Measurement, Fermi Energy Determination |
| 23EEL27 | Basic Electronics Engineering Laboratory | Lab | 1 | Diode Characteristics, Transistor Amplifier Circuits, Rectifier Circuits, Logic Gates Verification, Op-Amp Applications |
| 23KAN28 / 23ADD28 | Professional Kannada / Advanced English | Audit | 0 | Functional Kannada, Kannada Grammar and Literature, Advanced English Communication, Professional Writing, Cultural Communication |
| 23CST29 | Indian Constitution and Professional Ethics | Audit | 0 | Framing of Indian Constitution, Fundamental Rights and Duties, Ethical Theories, Engineering Ethics, Professional Code of Conduct |
| 23HES30 | Health and Wellness | Audit | 0 | Physical Health, Mental Wellness, Stress Management, Nutrition Basics, Healthy Lifestyle Practices |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML31 | Discrete Mathematics and Graph Theory | Core | 4 | Set Theory and Logic, Relations and Functions, Algebraic Structures, Graph Theory Fundamentals, Trees and Connectivity |
| 23AIML32 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs and Traversals, Sorting and Searching Algorithms |
| 23AIML33 | Object Oriented Programming with Java | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Collections Framework |
| 23AIML34 | Computer Organization and Architecture | Core | 3 | Digital Logic Circuits, CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining and Parallelism |
| 23AIML35 | Database Management Systems | Core | 3 | Database Concepts, ER Modeling, Relational Model, SQL Queries, Normalization and Transactions |
| 23AIML36 | Data Structures and Algorithms Lab | Lab | 1 | Stack and Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Graph Traversal Algorithms, Sorting and Searching Practice |
| 23AIML37 | Object Oriented Programming with Java Lab | Lab | 1 | Java Basics and OOP, Inheritance and Interfaces, Exception Handling Programs, File I/O in Java, GUI Applications |
| 23AIML38 | Database Management Systems Lab | Lab | 1 | SQL Data Definition Language, SQL Data Manipulation Language, Joins and Subqueries, Views and Stored Procedures, Transaction Control |
| 23AIML39 | Skill Enhancement Course - I | Core | 1 | Python Programming Fundamentals, Data Visualization with Python, Introduction to Version Control (Git), Linux Command Line Basics, Web Scraping Techniques |
| 23AIML40 | Internship - I | Audit | 0 | Industry Exposure, Professional Communication, Teamwork and Collaboration, Problem-solving in Industry, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML41 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis Techniques, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms and Backtracking |
| 23AIML42 | Operating Systems | Core | 3 | OS Structures and Services, Process Management, CPU Scheduling, Memory Management, File Systems and I/O |
| 23AIML43 | Artificial Intelligence | Core | 4 | Intelligent Agents, Problem Solving by Search, Heuristic Search, Game Playing, Knowledge Representation |
| 23AIML44 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| 23AIML45 | Probability and Statistics for AIML | Core | 3 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression Analysis |
| 23AIML46 | Artificial Intelligence Lab | Lab | 1 | Search Algorithms Implementation, Constraint Satisfaction Problems, Game Playing Agents, Prolog Programming, Knowledge Representation Systems |
| 23AIML47 | Machine Learning Lab | Lab | 1 | Data Preprocessing, Supervised Learning Implementation, Unsupervised Learning Implementation, Model Training and Testing, Evaluation Metrics Calculation |
| 23AIML48 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Creation and Management, Inter-process Communication, CPU Scheduling Algorithms, Memory Management Algorithms |
| 23AIML49 | Skill Enhancement Course - II | Core | 1 | R Programming for Data Analysis, Advanced Data Cleaning Techniques, Cloud Computing Basics, Ethical Hacking Fundamentals, Data Storytelling |
| 23AIML50 | Professional Practices | Audit | 0 | Professional Ethics, Technical Communication, Teamwork and Collaboration, Time Management, Career Planning |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML51 | Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 23AIML52 | Computer Networks | Core | 4 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer |
| 23AIML53 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| 23AIML54X | Professional Elective - I | Elective | 3 | Computer Vision / Natural Language Processing, Reinforcement Learning / Data Warehousing, Image Processing Techniques, Text Classification and Analysis, Q-learning and Policy Gradients |
| 23AIML55X | Open Elective - I | Elective | 3 | Chosen from a pool of open electives offered by other departments/VTU |
| 23AIML56 | Deep Learning Lab | Lab | 1 | Implementing Neural Networks, Convolutional Neural Networks for Image tasks, Recurrent Neural Networks for Sequence tasks, Transfer Learning Techniques, Deep Learning Model Evaluation |
| 23AIML57 | Computer Networks Lab | Lab | 1 | Network Commands and Utilities, Socket Programming, Network Traffic Analysis, Router and Switch Configuration, Client-Server Application Development |
| 23AIML58 | Mini Project - I | Project | 2 | Problem Identification, Project Design and Planning, Implementation and Testing, Technical Report Writing, Presentation Skills |
| 23AIML59 | Skill Enhancement Course - III | Core | 1 | Ethical AI Principles, Explainable AI (XAI) Concepts, Bias and Fairness in AI, AI Governance and Regulations, Data Privacy and Security in AI |
| 23AIML60 | Universal Human Values | Audit | 0 | Self-Exploration and Harmony, Understanding Values, Ethics in Human Conduct, Societal Values, Professional Ethics and Values |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML61 | Web Technologies | Core | 4 | HTML, CSS, JavaScript Fundamentals, Front-end Development Frameworks, Back-end Development, Database Integration, RESTful APIs |
| 23AIML62 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, Apache Spark, NoSQL Databases, Stream Processing |
| 23AIML63X | Professional Elective - II | Elective | 3 | AI in Robotics / Speech Processing, Generative AI / Cloud Computing for AI/ML, Robot Kinematics and Control, Speech Recognition and Synthesis, GANs and VAEs |
| 23AIML64X | Professional Elective - III | Elective | 3 | Explainable AI / Game AI, Time Series Analysis / Computer Graphics, Interpretability Techniques (SHAP, LIME), Pathfinding and Decision Making in Games, ARIMA Models and Forecasting |
| 23AIML65X | Open Elective - II | Elective | 3 | Chosen from a pool of open electives offered by other departments/VTU |
| 23AIML66 | Big Data Analytics Lab | Lab | 1 | Hadoop HDFS Operations, MapReduce Programming, Apache Spark Applications, Hive and Pig Scripting, Data Visualization Tools |
| 23AIML67 | Web Technologies Lab | Lab | 1 | HTML, CSS, JavaScript Projects, Front-end Frameworks (e.g., React), Server-side Scripting (e.g., Node.js), Database Connectivity, Building REST APIs |
| 23AIML68 | Internship - II / Mini Project - II | Internship/Project | 2 | Industry Best Practices, Advanced Project Development, Technical Documentation, Team Collaboration, Presentation and Defense |
| 23AIML69 | Skill Enhancement Course - IV | Core | 1 | Docker and Kubernetes, DevOps for Machine Learning, AI Ethics and Law, Patenting in AI, Research Methodology |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML71 | Internet of Things | Core | 4 | IoT Architecture, Sensors and Actuators, IoT Communication Protocols, Cloud Platforms for IoT, Edge Computing in IoT |
| 23AIML72 | Data Privacy and Security in AI | Core | 3 | Data Privacy Regulations (GDPR, India''''s DPA), Anonymization Techniques, Differential Privacy, Federated Learning, Adversarial Attacks and Defenses in AI |
| 23AIML73X | Professional Elective - IV | Elective | 3 | Robotics Process Automation (RPA) / Bio-Inspired AI, Blockchain for AI/ML / Quantum Computing for AI/ML, RPA Bot Development, Genetic Algorithms, Decentralized AI |
| 23AIML74X | Professional Elective - V | Elective | 3 | Human Computer Interaction / Agent-Based Modeling, Digital Forensics / Cognitive Computing, User Experience (UX) Design, Multi-Agent Systems, Cybercrime Investigation |
| 23AIML75 | Project Work Phase - I | Project | 4 | Literature Survey, Problem Definition, System Design, Module Implementation, Progress Reporting |
| 23AIML76 | Internship / Research Project (8 Weeks) | Internship/Project | 6 | Industry Work Experience, Advanced Research Methodology, Problem-solving in real-world scenarios, Comprehensive Technical Report, Viva Voce Examination |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIML81X | Professional Elective - VI | Elective | 3 | Cyber Physical Systems / Biomedical AI, Edge AI / Cognitive Robotics, CPS Architecture and Security, AI in Medical Imaging, On-device Machine Learning |
| 23AIML82 | Project Work Phase - II | Project | 8 | Final System Implementation, Testing and Validation, Performance Optimization, Deployment Strategies, Project Defense and Documentation |
| 23AIML83 | Technical Seminar | Seminar | 1 | Advanced Topic Research, Literature Review, Seminar Presentation Skills, Technical Question Answering, Report Preparation |
