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B-TECH in Ai Ml at JAIN (Deemed-to-be University)
Bengaluru, Karnataka
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About the Specialization
What is AI & ML at JAIN (Deemed-to-be University) Bengaluru?
This B.Tech in Artificial Intelligence and Machine Learning program at JAIN University focuses on equipping students with expertise in cutting-edge AI technologies and machine learning algorithms. Given India''''s burgeoning tech sector, this specialization is designed to meet the high demand for skilled professionals who can innovate and solve complex problems across various industries, emphasizing practical application and industry relevance.
Who Should Apply?
This program is ideal for aspiring engineers and innovators passionate about developing intelligent systems. It targets fresh 10+2 graduates with a strong aptitude for mathematics and programming, seeking entry into the dynamic fields of AI and ML. Working professionals looking to upskill in advanced analytical techniques or career changers transitioning into data-driven roles within the Indian tech landscape will also find this curriculum beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as AI Engineers, Machine Learning Scientists, Data Analysts, and Robotics Engineers. Entry-level salaries typically range from INR 6-10 lakhs per annum, with experienced professionals earning significantly more. The program prepares students for roles in top Indian and multinational tech companies, contributing to rapid professional growth and potentially leading to specialized certifications.
Student Success Practices
Foundation Stage
Master Core Programming Fundamentals- (Semester 1-2)
Dedicate significant effort to mastering C, Java, and Python programming languages, along with data structures and algorithms. Participate in coding competitions regularly to enhance problem-solving speed and accuracy. Leverage platforms like HackerRank and CodeChef for consistent practice.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, JAIN University''''s programming labs
Career Connection
Strong programming and DSA skills are foundational for cracking technical interviews at top tech companies in India and form the backbone for advanced AI/ML concepts.
Build a Robust Mathematical Foundation- (Semester 1-2)
Pay close attention to Linear Algebra, Calculus, Probability, and Discrete Mathematics courses. These are critical for understanding the underlying principles of AI and ML algorithms. Form study groups to tackle complex problems and utilize online resources like Khan Academy.
Tools & Resources
Khan Academy, NPTEL lectures on Mathematics, Online calculators
Career Connection
A solid mathematical background is essential for comprehending advanced ML models, research, and for roles in quantitative finance or core AI development.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Actively participate in group projects and form peer learning circles. Discussing concepts and jointly solving problems can deepen understanding and improve teamwork skills. Utilize university''''s internal forums for academic discussions.
Tools & Resources
GitHub for code collaboration, Discord/WhatsApp for group discussions, University library resources
Career Connection
Collaboration skills are highly valued in the industry, and peer learning reinforces concepts, preparing students for team-based engineering roles.
Intermediate Stage
Develop Practical ML/AI Projects and Portfolio- (Semester 3-5)
Beyond coursework, initiate personal projects applying machine learning and deep learning concepts. Focus on real-world datasets from platforms like Kaggle. Document your projects thoroughly on GitHub to showcase your practical skills to potential employers.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, GitHub, TensorFlow/PyTorch
Career Connection
A strong project portfolio is crucial for demonstrating practical ML/AI expertise and significantly enhances job applications for roles in data science and AI engineering.
Seek Early Industry Exposure via Internships/Workshops- (Semester 3-5)
Look for short-term internships, workshops, or bootcamps in AI/ML during summer or winter breaks. Even volunteer work on data science projects can provide invaluable industry insights and networking opportunities. Leverage JAIN University''''s career services.
Tools & Resources
LinkedIn, Internshala, JAIN University Career Services, Industry workshops
Career Connection
Early exposure helps understand industry demands, build professional networks, and often leads to pre-placement offers or full-time roles upon graduation.
Participate in Hackathons and Technical Competitions- (Semester 3-5)
Actively participate in hackathons and AI/ML competitions organized by colleges, companies, or platforms. These events challenge problem-solving abilities, encourage rapid prototyping, and expose students to diverse industry problems. Look for inter-university tech fests.
Tools & Resources
Devpost, ML India, Kaggle Competitions, University tech fests
Career Connection
Winning or even participating in such events enhances resumes, provides recognition, and helps in networking with industry experts and recruiters.
Advanced Stage
Specialize and Gain Certifications- (Semester 6-8)
Identify a niche within AI/ML (e.g., NLP, Computer Vision, Reinforcement Learning) and pursue advanced online courses and certifications from reputable platforms. This deepens expertise and provides a competitive edge in specific job roles. Focus on vendor-neutral or platform-specific certifications.
Tools & Resources
Coursera, edX, Udemy, NVIDIA Deep Learning Institute, Google Cloud AI certifications
Career Connection
Specialized knowledge and certifications are highly sought after by companies looking for experts in specific AI domains, leading to better career prospects and higher salaries.
Network Actively and Engage with AI/ML Communities- (Semester 6-8)
Attend industry conferences, seminars, and meetups in Bengaluru and other tech hubs. Connect with professionals, alumni, and thought leaders in the AI/ML space. Join online communities and forums to stay updated and seek mentorship. Utilize JAIN University''''s alumni network.
Tools & Resources
LinkedIn, Meetup.com, AI/ML conferences (e.g., GTC, DSSP), JAIN alumni portals
Career Connection
Networking is vital for discovering hidden job opportunities, gaining mentorship, and staying relevant with industry trends, often opening doors that conventional job searches cannot.
Focus on Placement Preparation and Mock Interviews- (Semester 6-8)
Start preparing for placements early. Practice aptitude, technical, and HR interview questions. Participate in mock interviews conducted by the university''''s placement cell or external career services. Tailor your resume and cover letter for specific AI/ML roles. Work on soft skills.
Tools & Resources
JAIN University Placement Cell, LeetCode for coding, Glassdoor for company insights, InterviewBit
Career Connection
Thorough preparation and practicing interview scenarios significantly increase the chances of securing desirable placements in leading tech and AI companies.
Program Structure and Curriculum
Eligibility:
- Pass in PUC / 10+2 / equivalent examination with Physics and Mathematics as compulsory subjects and should have obtained at least 45% marks (40% for reserved category) in aggregate in Physics, Mathematics and any one of the following subjects: Chemistry, Biology, Biotechnology, Computer Science, Electronics, Information Technology. Valid score in JEE (Main) / JEE (Advanced) / CET / Uni-GAUGE / JAIN Entrance Test (JET).
Duration: 8 semesters / 4 years
Credits: 175 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UMA101 | Linear Algebra and Calculus | Core | 4 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, Differential Calculus, Integral Calculus, Multivariable Calculus |
| 20UHS101 | Technical English | Core | 2 | Communication Skills, Technical Writing, Vocabulary and Grammar, Presentation Skills, Report Writing, Professional Etiquette |
| 20UCY101 | Engineering Chemistry | Core | 4 | Water Technology, Electrochemistry, Corrosion and Its Control, Energy Sources, Polymer Chemistry, Instrumental Methods |
| 20UCP101 | Programming for Problem Solving | Core | 4 | C Programming Fundamentals, Data Types and Operators, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| 20UME101 | Basic Mechanical Engineering | Core | 3 | Thermodynamics Basics, Internal Combustion Engines, Refrigeration and Air Conditioning, Power Transmission, Engineering Materials, Manufacturing Processes |
| 20UCL101 | Engineering Chemistry Laboratory | Lab | 1 | Volumetric Analysis, Instrumental Analysis, pH Metry, Conductometry, Colorimetry, Synthesis of Polymers |
| 20UPL101 | Programming for Problem Solving Laboratory | Lab | 1 | C Programming Exercises, Debugging Techniques, Problem Solving with C, Flowchart and Algorithms, Using IDEs |
| 20UWX101 | Work Shop Practice | Lab | 1 | Carpentry Shop, Fitting Shop, Welding Shop, Foundry Shop, Sheet Metal Shop, Basic Machining |
| 20UCV101 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Computer Aided Drafting, Conic Sections |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UMA201 | Advanced Calculus and Transforms | Core | 4 | Partial Differential Equations, Fourier Series, Fourier Transforms, Z-Transforms, Vector Calculus, Numerical Methods |
| 20UPH201 | Engineering Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers and Applications, Optical Fibers, Nanomaterials, Superconductivity |
| 20UEC201 | Basic Electronics | Core | 4 | Diode Circuits, Transistor Characteristics, Rectifiers and Filters, Amplifiers and Oscillators, Operational Amplifiers, Digital Electronics Basics |
| 20UEE201 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines, Power Systems Introduction |
| 20UCS201 | Data Structures and Applications | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Traversals, Sorting Algorithms, Searching Algorithms |
| 20UPH202 | Engineering Physics Laboratory | Lab | 1 | Optics Experiments, Electricity and Magnetism, Electronics Circuits, Modern Physics, Semiconductor Devices |
| 20UEL201 | Basic Electronics Laboratory | Lab | 1 | Diode Characteristics, Transistor Characteristics, Rectifier Circuits, Logic Gates Implementation, Op-Amp Applications |
| 20UCL201 | Data Structures Laboratory | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting and Searching, Recursion Applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UMA301 | Discrete Mathematics | Core | 3 | Mathematical Logic, Set Theory and Relations, Functions and Sequences, Graph Theory, Combinatorics, Algebraic Structures |
| 20UCS301 | Digital Logic Design | Core | 3 | Boolean Algebra, Logic Gates and K-Maps, Combinational Circuits, Sequential Circuits, Registers and Counters, Memories and PLDs |
| 20UCS302 | Database Management Systems | Core | 4 | DBMS Architecture, ER Model, Relational Model, Structured Query Language (SQL), Normalization, Transaction Management |
| 20UCS303 | Object Oriented Programming with Java | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Multithreading, Collections Framework |
| 20UCS304 | Computer Organization and Architecture | Core | 4 | Basic Computer Organization, CPU Design, Memory Hierarchy, Input/Output Organization, Pipelining, Instruction Set Architectures |
| 20UCS305 | Principles of Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Expert Systems |
| 20UCL301 | Database Management Systems Laboratory | Lab | 1 | SQL Queries and Commands, Database Design, PL/SQL Programming, Data Definition Language, Data Manipulation Language |
| 20UCL302 | Object Oriented Programming with Java Laboratory | Lab | 1 | Java Program Development, OOP Implementation, GUI Applications with AWT/Swing, Exception Handling Practices, Multithreading Applications |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UMA401 | Probability and Statistics for Engineers | Core | 3 | Probability Theory, Random Variables and Distributions, Joint Probability, Hypothesis Testing, Correlation and Regression, Markov Chains |
| 20UCS401 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| 20UCS402 | Operating Systems | Core | 4 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| 20UCS403 | Python Programming | Core | 3 | Python Fundamentals, Data Structures in Python, Functions and Modules, File Input/Output, Object-Oriented Python, NumPy and Pandas Basics |
| 20UCS404 | Fundamentals of Machine Learning | Core | 4 | Introduction to ML, Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation and Validation |
| 20UHS401 | Professional Ethics and Human Values | Core | 2 | Ethics in Engineering, Human Values, Professionalism, Corporate Social Responsibility, Environmental Ethics, Cyber Ethics |
| 20UCL401 | Python Programming Laboratory | Lab | 1 | Python Scripting, Data Manipulation using Pandas, Algorithmic Implementations, Web Scraping Basics, Data Visualization with Matplotlib |
| 20UCL402 | Machine Learning Laboratory | Lab | 1 | Implementation of ML Algorithms, Scikit-learn Library, Model Training and Testing, Data Preprocessing, Feature Engineering |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UMA501 | Linear Algebra for Machine Learning | Core | 3 | Vector Spaces and Subspaces, Matrix Decompositions, Eigenvalue Problems, Linear Transformations, Singular Value Decomposition, Optimization Basics |
| 20UCS501 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| 20UCS502 | Deep Learning | Core | 4 | Neural Networks Fundamentals, Activation Functions, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optimization Techniques |
| 20UCS503 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, MapReduce Framework, HDFS Architecture, Apache Spark, Data Streaming and Processing |
| 20UCS531 | Natural Language Processing | Elective | 3 | Text Preprocessing, N-grams and Language Models, Word Embeddings, POS Tagging, Named Entity Recognition, Text Classification |
| 20UHS501 | Research Methodology and IPR | Core | 2 | Research Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights, Patents and Trademarks, Research Ethics |
| 20UCL501 | Deep Learning Laboratory | Lab | 1 | Implementation of CNNs, RNNs using TensorFlow/Keras, Image Classification, Text Generation, Transfer Learning |
| 20UCL502 | Big Data Analytics Laboratory | Lab | 1 | Hadoop Ecosystem Setup, MapReduce Programs, Spark Data Processing, HDFS Operations, NoSQL Databases |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UCS601 | Cloud Computing | Core | 4 | Cloud Concepts and Models, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technology, Cloud Security, AWS/Azure Basics |
| 20UCS602 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Project Management, Agile Methodologies |
| 20UCS603 | Ethical Hacking and Cyber Security | Core | 3 | Cyber Security Fundamentals, Ethical Hacking Phases, Penetration Testing, Network Security, Web Application Security, Malware Analysis |
| 20UCS631 | Image Processing | Elective | 3 | Image Fundamentals, Image Filtering, Image Segmentation, Feature Extraction, Image Restoration, Image Compression |
| 20UOE601 | Entrepreneurship and Innovation | Elective | 3 | Entrepreneurship Concepts, Business Plan Development, Startup Ecosystem, Innovation Management, Funding Strategies, Marketing for Startups |
| 20UAI601 | Mini Project | Project | 2 | Project Planning, Design and Prototyping, Implementation and Testing, Documentation, Technical Presentation, Problem Identification |
| 20UCL601 | Cloud Computing Laboratory | Lab | 1 | AWS/Azure Services, Virtual Machine Setup, Cloud Storage Management, Network Configuration in Cloud, Serverless Computing |
| 20UCL602 | Project Management using Agile and DevOps Lab | Lab | 1 | Agile Project Tools (Jira), DevOps Practices, Continuous Integration/Deployment, Version Control with Git, Containerization (Docker) |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UCS701 | Natural Language Processing | Core | 4 | Text Preprocessing, N-grams and Language Models, Word Embeddings, POS Tagging, Named Entity Recognition, Neural NLP Models |
| 20UCS702 | Computer Vision | Core | 4 | Image Formation, Feature Detection and Description, Object Recognition, Image Segmentation, Motion Analysis, Deep Learning for Vision |
| 20UCS733 | Predictive Analytics | Elective | 3 | Regression Models, Classification Techniques, Time Series Analysis, Forecasting Methods, Model Deployment, Big Data Predictive Models |
| 20UCS741 | Internet of Things | Elective | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols (MQTT, CoAP), Cloud Platforms for IoT, IoT Security, Edge Computing |
| 20UOE701 | Green Computing | Elective | 3 | Energy Efficiency in IT, Sustainable Computing, E-waste Management, Green Data Centers, Power Management, Environmental Impact of IT |
| 20UAI701 | Internship / Project Phase 1 | Project | 4 | Industry Internship Experience, Problem Identification, Literature Survey, Initial Design and Prototyping, Project Proposal Writing, Feasibility Study |
| 20UCL701 | NLP and Computer Vision Laboratory | Lab | 1 | Implementation of NLP Tasks, Image Processing with OpenCV, NLTK and SpaCy Libraries, Object Detection, Sentiment Analysis |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20UAI801 | Major Project | Project | 12 | Advanced Project Design, System Implementation, Extensive Testing and Evaluation, Data Analysis and Interpretation, Thesis Writing, Final Project Presentation |
| 20UAI802 | Industry Internship | Internship | 6 | Real-world Problem Solving, Industry Environment Exposure, Professional Skill Development, Industry Best Practices, Technical Report Writing, Mentorship and Networking |
