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B-TECH in Artificial Intelligence Machine Learning at Alliance University
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at Alliance University Bengaluru?
This Artificial Intelligence & Machine Learning program at Alliance University, Bengaluru focuses on equipping students with deep knowledge and practical skills in cutting-edge AI and ML technologies. With India rapidly emerging as a global hub for technological innovation and AI adoption across sectors like healthcare, finance, and IT, this program is designed to meet the growing industry demand for skilled professionals. Its curriculum integrates foundational computer science with specialized AI/ML concepts, ensuring graduates are well-prepared for real-world challenges.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, programming, and problem-solving, seeking entry into the dynamic field of AI and Machine Learning. It also caters to working professionals aiming to upskill and transition into AI roles, provided they meet the foundational prerequisites. High school graduates who have excelled in PCM and possess a valid engineering entrance examination score are particularly well-suited, as the program builds from basic engineering principles.
Why Choose This Course?
Graduates of this program can expect diverse career paths such as AI Engineer, Machine Learning Scientist, Data Scientist, NLP Engineer, and Computer Vision Engineer in Indian and global companies. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The program’s emphasis on practical projects and industry-relevant curriculum enhances employability and aligns with certifications in deep learning, cloud AI, and big data, offering strong growth trajectories within the rapidly evolving Indian tech landscape.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand and practice fundamental programming concepts in C/Python and Object-Oriented Programming (OOP) using Java. Regularly solve coding challenges to build logic and problem-solving skills.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong programming skills are the bedrock for any AI/ML role, crucial for competitive coding rounds and developing efficient algorithms during placements.
Build a Strong Mathematical Base- (Semester 1-3)
Focus on excelling in Engineering Mathematics and Probability & Statistics. These subjects form the theoretical backbone for understanding AI/ML algorithms. Form study groups to tackle complex problems.
Tools & Resources
Khan Academy, MIT OpenCourseware, textbooks, peer study groups
Career Connection
A solid grasp of linear algebra, calculus, and statistics is essential for comprehending, debugging, and innovating in machine learning models, leading to better research and development roles.
Engage in Interdisciplinary Exploration- (Semester 1-2)
Actively participate in introductory workshops or clubs related to AI, Robotics, or IoT. Explore basic concepts and tools to broaden your technical horizon and discover areas of interest beyond the core curriculum.
Tools & Resources
University technical clubs, online introductory courses (Coursera, Udemy), basic robotics kits
Career Connection
Early exposure helps identify passions and can guide elective choices, leading to a more focused and engaging academic journey and clearer career aspirations.
Intermediate Stage
Hands-on Data Science Projects- (Semester 3-5)
Apply theoretical knowledge from Data Structures, Databases, and introductory AI/ML courses by undertaking mini-projects. Work on real datasets, clean them, perform exploratory data analysis, and build basic predictive models.
Tools & Resources
Kaggle datasets, GitHub for version control, Python libraries (Pandas, NumPy, Scikit-learn), Google Colab
Career Connection
Practical project experience is highly valued by recruiters for internships and entry-level data science/ML engineer roles, showcasing problem-solving and application skills.
Network and Seek Mentorship- (Semester 4-6)
Attend industry seminars, workshops, and tech talks organized by the university or local tech communities in Bengaluru. Connect with faculty, alumni, and industry professionals on LinkedIn to gain insights and potential mentorship.
Tools & Resources
LinkedIn, university career services, local tech meetups (e.g., Bengaluru AI Meetup)
Career Connection
Networking opens doors to internship opportunities, industry insights, and future job referrals, critical for navigating the competitive Indian tech job market.
Specialize through Electives and Online Certifications- (Semester 5-6)
Strategically choose professional electives that align with your emerging interests in AI/ML (e.g., Data Science, Big Data). Supplement these with specialized online certifications in areas like Deep Learning, NLP, or Computer Vision.
Tools & Resources
Coursera (DeepLearning.AI specialization), edX, NVIDIA DLI, AWS/Azure/GCP AI certifications
Career Connection
Demonstrates commitment to a specific sub-field of AI, making you a more attractive candidate for specialized roles and advanced studies.
Advanced Stage
Capstone Project & Portfolio Development- (Semester 7-8)
Undertake a significant capstone project (Project Work Phase I & II) that solves a real-world problem using advanced AI/ML techniques. Document your work meticulously and build a strong online portfolio on GitHub/personal website.
Tools & Resources
GitHub, Medium/personal blog for project write-ups, cloud platforms (AWS, GCP, Azure)
Career Connection
A well-executed capstone project is often the highlight of a resume for experienced roles, showcasing ability to drive complex projects from conception to deployment, critical for product-based companies.
Intensive Placement Preparation- (Semester 7-8)
Actively prepare for technical interviews by solving advanced data structures, algorithms, and machine learning questions. Practice communication skills for HR rounds and behavioral questions. Participate in mock interviews.
Tools & Resources
InterviewBit, LeetCode (Hard problems), Glassdoor for company-specific interview experiences, university placement cell workshops
Career Connection
Targeted preparation significantly increases the chances of securing placements with top-tier companies offering competitive salary packages in the Indian market.
Contribute to Open Source / Research- (Semester 6-8)
Engage in open-source AI/ML projects or seek opportunities to assist faculty in research papers/projects. This demonstrates initiative, collaboration skills, and contributes to the broader AI community.
Tools & Resources
GitHub, arXiv, research labs at the university or external institutions
Career Connection
Participation in open source or research enhances your profile for R&D roles, graduate studies, and positions in innovative startups, showcasing intellectual curiosity and practical contribution.
Program Structure and Curriculum
Eligibility:
- A pass in 10 + 2 (PCM) with a minimum of 45% aggregate marks (40% for SC/ST). A valid score in JEE Main/ JEE Advanced/ Alliance University Engineering Entrance Test (AUEET)/ SAT/ ACT/ COMED-K/ Any other State-level Engineering Entrance Examination.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50% (for theory), 60% (for lab/project), External: 50% (for theory), 40% (for lab/project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 101 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Partial Differentiation, Integral Calculus, Multiple Integrals, Vector Calculus |
| 102 | Engineering Physics | Core (Choice Group 1) | 3 | Quantum Mechanics, Crystal Structure, Lasers, Optical Fibers, Dielectric and Magnetic Materials |
| 103 | Engineering Chemistry | Core (Choice Group 1) | 3 | Electrochemistry, Corrosion, Polymers, Water Technology, Fuels and Combustion |
| 104 | Basic Electrical Engineering | Core (Choice Group 2) | 3 | DC Circuits, AC Circuits, Three-Phase Systems, Electrical Machines, Measuring Instruments |
| 105 | Basic Electronics Engineering | Core (Choice Group 2) | 3 | Semiconductor Diodes, Transistors, Rectifiers, Amplifiers, Digital Logic Gates |
| 106 | Problem Solving & Programming in C | Core (Choice Group 3) | 3 | Introduction to Programming, Control Statements, Functions, Arrays, Pointers, Structures, File I/O |
| 107 | Problem Solving & Programming in Python for Engineers | Core (Choice Group 3) | 3 | Python Basics, Data Structures, Control Flow, Functions, Object-Oriented Programming, Modules |
| 108 | Professional Communication | Core | 2 | Communication Process, Verbal Communication, Non-Verbal Communication, Written Communication, Presentation Skills |
| 102L | Engineering Physics Lab | Lab (Choice Group 1 Lab) | 1 | Experiments on Wave Optics, Electricity and Magnetism, Modern Physics, Semiconductor Devices |
| 103L | Engineering Chemistry Lab | Lab (Choice Group 1 Lab) | 1 | Experiments on Volumetric Analysis, Instrumental Analysis, Materials Synthesis, Water Quality Testing |
| 104L | Basic Electrical Engineering Lab | Lab (Choice Group 2 Lab) | 1 | Ohm''''s Law Verification, Kirchhoff''''s Laws, Star-Delta Conversion, AC Circuit Analysis, DC Machine Characteristics |
| 105L | Basic Electronics Engineering Lab | Lab (Choice Group 2 Lab) | 1 | Diode Characteristics, Rectifier Circuits, Transistor Amplifier, Logic Gates Implementation |
| 106L | Problem Solving & Programming in C Lab | Lab (Choice Group 3 Lab) | 1 | Control Statements Implementation, Array and String Operations, Function Calls and Recursion, Pointer Arithmetic, File Handling |
| 107L | Problem Solving & Programming in Python Lab | Lab (Choice Group 3 Lab) | 1 | Python Data Structures, Conditional and Loop Structures, Function Definitions, Object-Oriented Concepts, Module Usage |
| 109 | Computer Aided Engineering Graphics | Lab | 2 | Engineering Curves, Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Commands |
| 110 | Universal Human Values | Non-Credit | 0 | Understanding Human Values, Harmony in Self, Family, Society, Nature, Professional Ethics, Holistic Living |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 201 | Engineering Mathematics – II | Core | 4 | Linear Algebra, Ordinary Differential Equations, Laplace Transforms, Fourier Series, Complex Analysis |
| 202 | Engineering Chemistry | Core (Alternate to Sem 1 Physics) | 3 | Electrochemistry, Corrosion, Polymers, Water Technology, Fuels and Combustion |
| 203 | Engineering Physics | Core (Alternate to Sem 1 Chemistry) | 3 | Quantum Mechanics, Crystal Structure, Lasers, Optical Fibers, Dielectric and Magnetic Materials |
| 204 | Basic Electronics Engineering | Core (Alternate to Sem 1 Electrical) | 3 | Semiconductor Diodes, Transistors, Rectifiers, Amplifiers, Digital Logic Gates |
| 205 | Basic Electrical Engineering | Core (Alternate to Sem 1 Electronics) | 3 | DC Circuits, AC Circuits, Three-Phase Systems, Electrical Machines, Measuring Instruments |
| 206 | Problem Solving & Programming in Python for Engineers | Core (Alternate to Sem 1 C) | 3 | Python Basics, Data Structures, Control Flow, Functions, Object-Oriented Programming, Modules |
| 207 | Problem Solving & Programming in C | Core (Alternate to Sem 1 Python) | 3 | Introduction to Programming, Control Statements, Functions, Arrays, Pointers, Structures, File I/O |
| 208 | Engineering Design | Core | 2 | Design Process, Product Life Cycle, Design Tools, Sustainable Design, Ergonomics |
| 202L | Engineering Chemistry Lab | Lab (Alternate to Sem 1 Physics Lab) | 1 | Experiments on Volumetric Analysis, Instrumental Analysis, Materials Synthesis, Water Quality Testing |
| 203L | Engineering Physics Lab | Lab (Alternate to Sem 1 Chemistry Lab) | 1 | Experiments on Wave Optics, Electricity and Magnetism, Modern Physics, Semiconductor Devices |
| 204L | Basic Electronics Engineering Lab | Lab (Alternate to Sem 1 Electrical Lab) | 1 | Diode Characteristics, Rectifier Circuits, Transistor Amplifier, Logic Gates Implementation |
| 205L | Basic Electrical Engineering Lab | Lab (Alternate to Sem 1 Electronics Lab) | 1 | Ohm''''s Law Verification, Kirchhoff''''s Laws, Star-Delta Conversion, AC Circuit Analysis, DC Machine Characteristics |
| 206L | Problem Solving & Programming in Python Lab | Lab (Alternate to Sem 1 C Lab) | 1 | Python Data Structures, Conditional and Loop Structures, Function Definitions, Object-Oriented Concepts, Module Usage |
| 207L | Problem Solving & Programming in C Lab | Lab (Alternate to Sem 1 Python Lab) | 1 | Control Statements Implementation, Array and String Operations, Function Calls and Recursion, Pointer Arithmetic, File Handling |
| 209 | Engineering Workshop Practice | Lab | 2 | Carpentry, Fitting, Welding, Machining, Sheet Metal Work |
| 210 | Environmental Science | Non-Credit | 0 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 301 | Engineering Mathematics – III | Core | 4 | Partial Differential Equations, Fourier Transforms, Z-Transforms, Numerical Methods, Probability and Statistics |
| 302 | Discrete Mathematics | Core | 3 | Logic, Set Theory, Relations and Functions, Graph Theory, Algebraic Structures |
| 303 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| 304 | Object-Oriented Programming using Java | Core | 4 | Classes and Objects, Inheritance, Polymorphism, Interfaces, Packages, Exception Handling, Collections Framework |
| 305 | Database Management Systems | Core | 4 | Relational Model, SQL Queries, Normalization, Transaction Management, Concurrency Control |
| 303L | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting and Searching Practice |
| 304L | Object-Oriented Programming using Java Lab | Lab | 1 | Java Class Design, Inheritance and Interface Examples, Exception Handling Programs, File I/O in Java, GUI Applications with AWT/Swing |
| 305L | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, Advanced SQL Queries, Stored Procedures, Database Design Practice |
| 306 | Skill Enhancement Course – I | Skill | 1 | |
| 307 | Internship – I | Project | 1 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 401 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| 402 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| 403 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| 404 | Computer Networks | Core | 4 | OSI/TCP-IP Model, Network Topologies, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| 405 | Web Technologies | Core | 3 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation, Web Servers and Hosting, Client-Server Architecture |
| 403L | Operating Systems Lab | Lab | 1 | Linux Commands, Shell Scripting, Process Synchronization, CPU Scheduling Algorithms, Memory Management Techniques |
| 404L | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis, Routing Protocols, Network Security Tools |
| 405L | Web Technologies Lab | Lab | 1 | Static Web Page Design, Dynamic Content with JavaScript, Form Validation, Responsive Design, AJAX Implementation |
| 406 | Skill Enhancement Course – II | Skill | 1 | |
| 407 | Internship – II | Project | 1 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 501AIML | AI & ML Mathematics | Core | 4 | Linear Algebra for ML, Probability and Statistics for ML, Calculus for ML, Optimization Techniques, Vector Spaces and Norms |
| 502AIML | Artificial Intelligence | Core | 4 | Problem Solving Agents, Search Algorithms (informed/uninformed), Knowledge Representation, First-Order Logic, Introduction to Machine Learning |
| 503AIML | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| 504AIML | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Decidability and Undecidability |
| AUAIPE101 | Introduction to Data Science | Elective (Professional Elective – I) | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Introduction to Predictive Modeling |
| OE – I | Open Elective – I | Elective | 3 | |
| 502AIMLL | Artificial Intelligence Lab | Lab | 1 | Implementing Search Algorithms, Logic Programming with Prolog, Game Playing AI, Constraint Satisfaction Problems |
| 503AIMLL | Machine Learning Lab | Lab | 1 | Regression Model Implementation, Classification Model Implementation, Clustering Techniques, Feature Engineering, Model Evaluation Metrics |
| 505AIML | Mini Project – I | Project | 1 | |
| 506AIML | Internship – III | Project | 1 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 601AIML | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs |
| 602AIML | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models (N-grams), Word Embeddings (Word2Vec, GloVe), Sequence Models (RNNs, LSTMs), Text Classification and Sentiment Analysis |
| 603AIML | Reinforcement Learning | Core | 4 | Markov Decision Processes, Value Iteration and Policy Iteration, Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods |
| AUAIPE102 | Big Data Analytics | Elective (Professional Elective – II) | 3 | Big Data Concepts and Challenges, Hadoop Ecosystem, MapReduce Programming, Apache Spark, Data Warehousing and Data Lakes |
| OE – II | Open Elective – II | Elective | 3 | |
| 601AIMLL | Deep Learning Lab | Lab | 1 | Implementing CNNs for Image Classification, RNNs for Sequence Prediction, Transfer Learning, TensorFlow/PyTorch Basics |
| 602AIMLL | Natural Language Processing Lab | Lab | 1 | Text Preprocessing with NLTK/SpaCy, Word Embedding Generation, Named Entity Recognition, Text Generation Models |
| 603AIMLL | Reinforcement Learning Lab | Lab | 1 | Q-Learning Implementation, SARSA Algorithm, OpenAI Gym Environments, Deep Reinforcement Learning basics |
| 604AIML | Mini Project – II | Project | 1 | |
| 605AIML | Internship – IV | Project | 1 |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 701AIML | Computer Vision | Core | 4 | Image Processing Fundamentals, Feature Detection and Description, Object Recognition, Image Segmentation, Deep Learning for Vision |
| 702AIML | Machine Learning Operations (MLOps) | Core | 4 | ML Lifecycle Management, Model Deployment Strategies, Monitoring and Logging ML Models, Version Control for ML Assets, CI/CD for Machine Learning |
| AUAIPE103 | Cloud Computing for AI | Elective (Professional Elective – III) | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization and Containers, Serverless Computing, AI Services on AWS/Azure/GCP, Data Storage and Processing in Cloud |
| AUAIPE104 | Robotics and AI | Elective (Professional Elective – IV) | 3 | Robot Kinematics and Dynamics, Sensors and Actuators, Motion Planning, Robot Learning, Human-Robot Interaction |
| OE – III | Open Elective – III | Elective | 3 | |
| 701AIMLL | Computer Vision Lab | Lab | 1 | Image Filtering Techniques, Edge Detection, Object Tracking, Image Stitching, OpenCV Library Usage |
| 702AIMLL | MLOps Lab | Lab | 1 | Setting up ML Pipelines, Model Versioning and Registry, Containerization (Docker), Deployment to Cloud Platforms |
| 703AIML | Project Work Phase – I | Project | 3 | |
| 704AIML | Internship – V | Project | 1 |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AUAIPE105 | Ethical AI | Elective (Professional Elective – V) | 3 | AI Ethics Principles, Bias and Fairness in AI, Accountability and Transparency, AI Governance and Regulations, Privacy in AI Systems |
| OE – IV | Open Elective – IV | Elective | 3 | |
| 801AIML | Project Work Phase – II | Project | 8 | |
| 802AIML | Internship – VI | Project | 1 |
