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B-TECH in Artificial Intelligence Machine Learning at CHRIST (Deemed to be University)
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at CHRIST (Deemed to be University) Bengaluru?
This B.Tech Artificial Intelligence & Machine Learning program at CHRIST, Bengaluru focuses on equipping students with advanced knowledge and practical skills in AI, ML, and Deep Learning. Given India''''s burgeoning tech sector, this program is designed to meet the high demand for skilled professionals in areas like data science, intelligent systems, and automation. It emphasizes a strong theoretical foundation coupled with hands-on project experience, preparing graduates for diverse roles in the evolving digital landscape.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics, logical reasoning, and a keen interest in technology and problem-solving. It also caters to those aspiring to become data scientists, machine learning engineers, AI researchers, or intelligent system developers in Indian and global tech companies. A prerequisite background in science (PCM) from 10+2 is essential, along with a desire to innovate and contribute to India''''s AI-driven growth.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths such as AI Engineer, ML Scientist, Data Analyst, Robotics Engineer, or NLP Specialist. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA in top Indian tech hubs. The program fosters critical thinking, problem-solving, and practical application skills, aligning with industry demand for professionals capable of driving innovation in various sectors.
Student Success Practices
Foundation Stage
Master Programming Fundamentals & Data Structures- (Semester 1-2)
Consistently practice programming concepts in C and Python, focusing on logic building, algorithms, and data structures. Utilize online coding platforms like HackerRank and LeetCode. Build small projects to apply theoretical knowledge, such as implementing sorting algorithms or basic data structures from scratch.
Tools & Resources
CodeChef, GeeksforGeeks, HackerRank, LeetCode, C and Python IDEs (VS Code)
Career Connection
Strong fundamentals are critical for technical interviews and building efficient solutions, forming the backbone for advanced AI/ML concepts.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Dedicate time to deeply understand Linear Algebra, Calculus, Probability, and Statistics. These are the mathematical pillars of AI/ML. Solve problems from textbooks and online courses. Seek extra help from professors or peers for challenging topics.
Tools & Resources
Khan Academy, NPTEL courses, Specific math textbooks, Academic support groups
Career Connection
A solid math foundation is indispensable for comprehending ML algorithms, optimizing models, and excelling in quantitative roles.
Engage in Peer Learning & Technical Clubs- (Semester 1-2)
Actively participate in study groups, departmental technical clubs, and coding competitions. Collaborating with peers helps in clarifying doubts, learning new perspectives, and developing teamwork skills. Attend introductory workshops on AI/ML.
Tools & Resources
University technical clubs, Study circles, Inter-college tech events
Career Connection
Develops communication and collaboration skills, expands network, and provides early exposure to AI/ML concepts, useful for future projects and jobs.
Intermediate Stage
Hands-on with AI/ML Frameworks & Projects- (Semester 3-5)
Beyond theoretical understanding of AI/ML, focus on practical implementation using Python libraries (Numpy, Pandas, Scikit-learn, TensorFlow, Keras). Work on guided projects and Kaggle datasets to build proficiency in model development, training, and evaluation.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, TensorFlow, Keras, PyTorch, Scikit-learn
Career Connection
Direct experience with industry-standard tools and real datasets makes candidates highly desirable for internships and entry-level ML engineering roles.
Participate in Hackathons & Competitions- (Semester 3-5)
Regularly participate in AI/ML hackathons, coding challenges, and innovation competitions. These platforms provide exposure to real-world problems, foster rapid prototyping skills, and offer networking opportunities with industry professionals and recruiters.
Tools & Resources
Major hackathon platforms (e.g., MLH, Devfolio), University-organized tech fests
Career Connection
Builds a strong project portfolio, demonstrates problem-solving under pressure, and can lead to direct recruitment opportunities.
Seek Industry Mentorship & Networking- (Semester 3-5)
Connect with AI/ML professionals through LinkedIn, university alumni networks, and industry events. Seek mentorship to understand career paths, gain insights into industry trends, and prepare for future roles. Attend industry webinars and seminars.
Tools & Resources
LinkedIn, University alumni portal, Industry meetups (e.g., local AI/ML groups)
Career Connection
Opens doors to internship and job opportunities, provides guidance on skill development, and helps build professional relationships.
Advanced Stage
Specialize through Advanced Projects & Research- (Semester 6-8)
Choose a specific area within AI/ML (e.g., NLP, Computer Vision, Reinforcement Learning) for your major project and delve deep. Consider publishing research papers or contributing to open-source projects. Focus on developing a niche expertise.
Tools & Resources
Research journals (e.g., IEEE, ACM), arXiv, GitHub, Specialized AI/ML libraries
Career Connection
Establishes expertise, enhances resume with tangible contributions, and can lead to research positions or highly specialized industry roles.
Internship and Real-world Problem Solving- (Semester 7)
Secure meaningful internships in AI/ML roles at reputable companies. Focus on applying academic knowledge to solve real business problems, understanding deployment challenges, and working in a professional team environment. Document achievements diligently.
Tools & Resources
Company career portals, University placement cell, LinkedIn
Career Connection
Converts theoretical knowledge into practical experience, often leading to pre-placement offers, and provides invaluable industry exposure.
Master Interview Skills & Portfolio Building- (Semester 7-8)
Prepare rigorously for technical and HR interviews, practicing mock interviews and refining your communication skills. Build a comprehensive portfolio showcasing your projects, contributions, and skills on platforms like GitHub or personal websites.
Tools & Resources
Interview preparation platforms (e.g., AlgoExpert, InterviewBit), GitHub, Personal portfolio website, University career services
Career Connection
Essential for converting opportunities into job offers, presenting capabilities effectively, and demonstrating readiness for industry roles.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 with an aggregate of 50% marks in Physics, Chemistry and Mathematics (PCM) from any recognised Board in India. Students pursuing International curriculum must have AIU approval and a grade of not less than D in Physics, Chemistry and Mathematics (PCM) in A Level.
Duration: 8 semesters / 4 years
Credits: 180 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB1MAT101 | Linear Algebra & Calculus | Core | 4 | Matrices and Systems of Equations, Vector Spaces, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Multivariable Calculus |
| BTB1CHY101 | Engineering Chemistry | Core | 4 | Water Technology, Energy Sources, Electrochemistry, Corrosion and its Control, Engineering Materials, Polymer Chemistry |
| BTB1PHE101 | Engineering Physics | Core | 4 | Oscillations and Waves, Optics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Nuclear Physics |
| BTB1CSE101 | Programming for Problem Solving | Core | 4 | Introduction to Programming, Control Structures, Functions, Arrays, Pointers, Structures and Unions, File Handling |
| BTB1CSE102 | Programming for Problem Solving Lab | Lab | 1 | C Language basics, Control flow implementation, Function implementation, Array and String manipulation, Pointer usage, Structure operations, File I/O |
| BTB1MEE101 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, Power Plants, IC Engines, Refrigeration, Production Engineering, Machine Elements |
| BTB1EEE101 | Basic Electrical and Electronics Engineering | Core | 4 | DC and AC Circuits, Network Theorems, Diodes and Transistors, Amplifiers, Digital Electronics, Transducers |
| BTB1ENG101 | English Language Skills Lab | Lab | 1 | Phonetics, Public Speaking, Group Discussion, Presentation Skills, Interview Skills, Report Writing |
| BTB1WSK101 | Workshop Practice | Lab | 1 | Carpentry, Fitting, Welding, Foundry, Sheet Metal, Machine Shop |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB2MAT201 | Probability & Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation |
| BTB2CHY201 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Instrumental Methods, Synthesis of Polymers, Corrosion Experiments, pH Metry, Conductometry |
| BTB2PHE201 | Engineering Physics Lab | Lab | 1 | Optics experiments, Semiconductor Devices, Magnetic Fields, Sound Waves, Quantum Phenomena |
| BTB2CSE201 | Data Structures | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Searching and Sorting |
| BTB2CSE202 | Data Structures Lab | Lab | 1 | Implementation of Arrays, Linked Lists, Stacks, Queues, Tree Traversal, Graph Algorithms, Sorting and Searching techniques |
| BTB2EEE201 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Verification of circuit laws, AC and DC circuit analysis, Diode characteristics, Transistor biasing, Logic gates |
| BTB2ECE201 | Digital Logic Design | Core | 3 | Number Systems, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Registers and Counters |
| BTB2CIE201 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, CAD tools |
| BTB2AIM201 | Introduction to AI & ML | Core | 4 | Overview of AI, Machine Learning Basics, Supervised Learning, Unsupervised Learning, Reinforcement Learning, AI Ethics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB3AIM301 | Discrete Mathematics for AI | Core | 4 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| BTB3AIM302 | Object Oriented Programming with Python | Core | 3 | Python Basics, OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, File I/O |
| BTB3AIM303 | Object Oriented Programming Lab with Python | Lab | 1 | Python programming exercises, Implementing OOP concepts, File handling in Python, Database connectivity |
| BTB3AIM304 | Computer Organization and Architecture | Core | 4 | Computer System Overview, CPU Organization, Memory System, I/O Organization, Pipelining, Parallel Processing |
| BTB3AIM305 | Operating Systems | Core | 4 | OS Introduction, Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, I/O Systems |
| BTB3AIM306 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Sorting Algorithms, Graph Algorithms, Dynamic Programming, Greedy Algorithms, NP-Completeness |
| BTB3ENV301 | Environmental Studies | Core | 2 | Ecosystems, Biodiversity, Environmental Pollution, Natural Resources, Social Issues and Environment, Environmental Ethics |
| BTB3MNG301 | Engineering Economics & Financial Management | Core | 2 | Demand and Supply, Market Structures, Macroeconomics, Capital Budgeting, Financial Ratios, Project Evaluation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB4AIM401 | Theory of Computation | Core | 4 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Decidability, Undecidability |
| BTB4AIM402 | Database Management Systems | Core | 4 | DBMS Architecture, ER Model, Relational Model, SQL, Normalization, Transaction Management, Concurrency Control |
| BTB4AIM403 | Database Management Systems Lab | Lab | 1 | SQL queries, Database design, Normalization implementation, Stored Procedures, Triggers, Front-end integration |
| BTB4AIM404 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression, Classification, Model Evaluation, Ensemble Methods, Feature Engineering |
| BTB4AIM405 | Machine Learning Lab | Lab | 1 | Python libraries for ML (Scikit-learn, Pandas), Data preprocessing, Implementing ML algorithms, Model training and evaluation |
| BTB4AIM406 | Artificial Intelligence | Core | 4 | AI Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Planning, Expert Systems, Game Playing |
| BTB4AIM407 | Artificial Intelligence Lab | Lab | 1 | Implementing search algorithms, Logic programming (Prolog), Knowledge representation, AI toolkits (NLTK, TensorFlow basics) |
| BTB4AIM408 | Data Mining & Data Warehousing | Core | 3 | Data Warehouse Architecture, ETL Process, OLAP, Data Preprocessing, Association Rules, Classification, Clustering |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB5AIM501 | Computer Networks | Core | 4 | Network Models (OSI/TCP-IP), Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer, Network Security |
| BTB5AIM502 | Deep Learning | Core | 4 | Neural Network Basics, Perceptrons, Backpropagation, CNNs, RNNs, LSTMs, Autoencoders, GANs, Transfer Learning |
| BTB5AIM503 | Deep Learning Lab | Lab | 1 | Implementing CNNs, RNNs, Using TensorFlow/Keras, Image Classification, Sequence Prediction, Generative Models |
| BTB5AIM504 | Applied Machine Learning | Core | 4 | Advanced ML models, Recommendation Systems, Natural Language Processing, Computer Vision, Time Series Analysis, Feature Engineering |
| BTB5AIM505 | Applied Machine Learning Lab | Lab | 1 | Case studies in NLP/CV, Building recommendation engines, Deployment of ML models, Cloud ML platforms |
| BTB5AIME102 | Program Elective I (Natural Language Processing) | Elective | 3 | Text Preprocessing, Word Embeddings, POS Tagging, Syntactic Parsing, Semantic Role Labeling, Machine Translation |
| BTB5OECXXX | Open Elective I | Elective | 3 | |
| BTB5AIM506 | Mini Project | Project | 2 | Problem Identification, Literature Survey, Design, Implementation, Testing, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB6AIM601 | Cloud Computing | Core | 4 | Cloud Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, AWS/Azure Basics |
| BTB6AIM602 | Ethics in AI & ML | Core | 3 | Ethical AI Principles, Bias in AI, Transparency and Explainability, Privacy Concerns, Legal Frameworks, Societal Impact |
| BTB6AIM603 | Research Methodology | Core | 2 | Research Problem, Literature Review, Research Design, Data Collection, Statistical Analysis, Report Writing, Plagiarism |
| BTB6AIME202 | Program Elective II (Robotics and Automation) | Elective | 3 | Robot Kinematics, Dynamics and Control, Path Planning, Robot Programming, Sensors and Actuators, Industrial Automation |
| BTB6OECXXX | Open Elective II | Elective | 3 | |
| BTB6AIM604 | Project Work - Phase I | Project | 6 | Problem Definition, Project Proposal, System Design, Module Development, Initial Implementation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB7AIME303 | Program Elective III (Generative AI) | Elective | 3 | Generative Models, Variational Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, Ethical Considerations |
| BTB7AIME403 | Program Elective IV (Quantum Computing) | Elective | 3 | Quantum Mechanics Review, Qubits and Superposition, Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography, Quantum Machine Learning |
| BTB7OECXXX | Open Elective III | Elective | 3 | |
| BTB7AIM701 | Internship / Industrial Training | Project | 6 | Real-world Project Experience, Industry Best Practices, Professional Skill Development, Mentorship, Report and Presentation |
| BTB7AIM702 | Project Work - Phase II | Project | 8 | Advanced Implementation, Testing, Performance Optimization, Documentation, Research Publication |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BTB8AIME501 | Program Elective V (Image and Video Analytics) | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Object Tracking, Video Segmentation, Real-time Analytics |
| BTB8OECXXX | Open Elective IV | Elective | 3 | |
| BTB8AIM801 | Project Work - Phase III | Project | 10 | Final Implementation, Thorough Testing, Deployment Strategies, User Documentation, Final Presentation, Publication |
