.png&w=1920&q=75)
B-TECH-ARTIFICIAL-INTELLIGENCE-MACHINE-LEARNING in General at ST. JOSEPH ENGINEERING COLLEGE
Dakshina Kannada, Karnataka
.png&w=1920&q=75)
About the Specialization
What is General at ST. JOSEPH ENGINEERING COLLEGE Dakshina Kannada?
This Artificial Intelligence and Machine Learning (AI&ML) program at St Joseph Engineering College focuses on equipping students with expertise in intelligent systems, data analysis, and predictive modeling. It emphasizes both theoretical foundations and practical applications relevant to India''''s burgeoning tech industry, covering areas from foundational programming to advanced deep learning algorithms. The program aims to foster innovation and develop professionals capable of addressing complex challenges across various sectors.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming, seeking entry into high-growth technology fields. It also suits working professionals looking to upskill in cutting-edge AI&ML domains, or career changers aspiring to transition into roles like Data Scientists, Machine Learning Engineers, or AI Researchers within the Indian IT landscape. Prerequisite backgrounds typically include a strong foundation in science or engineering.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative India-specific career paths such as AI Developers, ML Engineers, Data Analysts, or Business Intelligence Specialists. Entry-level salaries typically range from INR 4-8 Lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry demands, preparing students for roles in Indian startups, IT services giants, and product-based MNCs operating in the country.
Student Success Practices
Foundation Stage
Master Core Programming & Math- (Semester 1-2)
Focus intensely on ''''Programming for Problem Solving'''' (C language) and core mathematics like ''''Calculus & Differential Equations'''' and ''''Linear Algebra & Probability''''. Utilize online platforms like HackerRank, LeetCode, and Khan Academy to practice problem-solving, ensuring a robust foundation essential for advanced AI/ML concepts.
Tools & Resources
HackerRank, LeetCode, Khan Academy, GeeksforGeeks
Career Connection
This early mastery provides the analytical and computational bedrock crucial for understanding complex algorithms and securing internships.
Develop Strong Study Habits- (Semester 1-2)
Actively participate in lectures, review concepts regularly, and form study groups with peers. Engage in practical lab sessions for subjects like ''''Applied Physics'''' and ''''Elements of Electrical & Electronics Engineering'''' to build hands-on skills and experiential learning.
Tools & Resources
Peer Study Groups, College Library Resources, Lab Manuals
Career Connection
Effective time management and consistent effort in these initial semesters will build momentum for tackling more specialized subjects and achieving academic excellence.
Explore Basic Design Thinking- (Semester 1-2)
Apply principles learned in ''''Design Thinking & Innovation'''' to everyday problems, fostering creative problem-solving and critical thinking. Participating in college-level ideathons or mini-competitions can provide practical exposure to user-centric design.
Tools & Resources
IDEO Design Kit, Local Ideathons, College Clubs
Career Connection
Developing an innovative mindset and early problem-solving skills is valuable in product development roles and contributes to a robust portfolio.
Intermediate Stage
Deep Dive into AI/ML Fundamentals- (Semester 3-5)
Prioritize subjects like ''''Data Structures,'''' ''''Object-Oriented Programming with Java,'''' ''''Python Programming,'''' and especially ''''Machine Learning'''' and ''''Artificial Intelligence''''. Engage in independent projects using Python libraries like NumPy, Pandas, Scikit-learn, TensorFlow/Keras on platforms like Kaggle or GitHub.
Tools & Resources
Kaggle, GitHub, NumPy, Pandas, Scikit-learn, TensorFlow/Keras
Career Connection
This practical application solidifies theoretical knowledge, creates a compelling project portfolio, and makes students internship-ready for AI/ML roles.
Seek Industry Exposure & Networking- (Semester 3-5)
Actively pursue internships, particularly after the 4th or 6th semester. Attend workshops, guest lectures, and industry seminars organized by the college or local tech communities. Connect with alumni and professionals on LinkedIn to gain insights into industry trends and potential career opportunities in the Indian AI/ML landscape.
Tools & Resources
LinkedIn, College Placement Cell, Industry Workshops
Career Connection
Industry exposure and a strong professional network are vital for understanding real-world challenges and securing placement opportunities.
Participate in Coding & AI Challenges- (Semester 3-5)
Join coding competitions on platforms like CodeChef, TopCoder, or participate in AI/ML specific challenges on Kaggle. This enhances problem-solving speed, exposes students to diverse datasets and real-world problems, and helps in building a competitive profile.
Tools & Resources
CodeChef, TopCoder, Kaggle, HackerEarth
Career Connection
Success in these competitions is highly valued by recruiters and significantly boosts a student''''s profile for technical roles and advanced studies.
Advanced Stage
Specialized Project & Research- (Semester 6-8)
Dedicate significant effort to ''''Major Project Phase I & II'''', choosing a topic aligned with their desired specialization (e.g., Deep Learning, NLP, Computer Vision). Aim for innovative solutions, potentially leading to research publications or patent applications, showcasing advanced problem-solving skills.
Tools & Resources
Research Papers (arXiv, IEEE Xplore), Academic Mentors, Specialized Libraries
Career Connection
A strong final year project is a cornerstone for demonstrating expertise, securing competitive placements, and pursuing higher studies or entrepreneurial ventures.
Placement & Career Readiness- (Semester 6-8)
Focus on developing strong communication and interview skills, leveraging ''''Technical Communication'''' and ''''Professional Ethics & Social Responsibility'''' learnings. Prepare a compelling resume and portfolio showcasing projects and internships. Utilize college placement cells for mock interviews, resume reviews, and connecting with hiring companies.
Tools & Resources
College Placement Cell, Mock Interview Platforms, Resume Builders
Career Connection
Effective career preparation is critical for converting academic knowledge into successful job placements in top Indian tech companies and startups.
Continuous Learning & Advanced Certifications- (Semester 6-8)
Explore advanced electives like ''''Deep Learning'''', ''''Big Data Analytics'''', or ''''Explainable AI'''' to gain specialized knowledge. Consider pursuing industry certifications from providers like Google (TensorFlow Developer), AWS (Machine Learning Specialty), or NVIDIA (Deep Learning Institute) to enhance employability.
Tools & Resources
Coursera, edX, Udemy, Google Certifications, AWS Certifications
Career Connection
Staying updated with evolving AI technologies and acquiring industry-recognized certifications ensures long-term career growth and adaptability in the dynamic tech industry.
Program Structure and Curriculum
Eligibility:
- Pass in 2nd PUC / 12th Std / equivalent examination with English as one of the Languages and obtained a Minimum of 45% of Marks in aggregate in Physics and Mathematics as compulsory subjects along with Chemistry / Bio-Technology / Biology / Electronics / Computer Science / Information Technology / Technical Vocational subject. (40% for SC, ST, Cat-1, 2A, 2B, 3A and 3B category candidates).
Duration: 8 semesters / 4 years
Credits: 152 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA101 | Calculus & Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Partial Differential Equations, Vector Calculus, Ordinary Differential Equations |
| 23PC101 | Applied Physics | Core | 4 | Quantum Physics, Lasers and Fiber Optics, Engineering Materials, Superconductors and Dielectrics, Nanomaterials and Smart Materials |
| 23ES101 | Elements of Electrical & Electronics Engineering | Core | 4 | DC Circuits, AC Circuits, Electrical Machines, Semiconductor Devices, Digital Electronics |
| 23CS101 | Programming for Problem Solving | Core | 4 | Introduction to Programming, Control Structures, Functions and Arrays, Pointers and Strings, Structures and File Handling |
| 23HS101 | Communicative English | Core | 2 | Communication Skills, Grammar and Usage, Reading Comprehension, Writing Skills, Presentation Skills |
| 23SD101 | Design Thinking & Innovation | Skill Development | 2 | Introduction to Design Thinking, Empathize and Define, Ideate for Solutions, Prototype and Test, Innovation and Entrepreneurship |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23MA201 | Linear Algebra & Probability | Core | 4 | Matrices and Determinants, Vector Spaces, Linear Transformations, Probability Distributions, Random Variables and Stochastic Processes |
| 23PC201 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion and its Control, Energy Storage Devices, Water Technology, Polymer Chemistry |
| 23ME201 | Elements of Mechanical Engineering | Core | 4 | Thermodynamics Principles, IC Engines and Power Plants, Refrigeration and Air Conditioning, Power Transmission Systems, Manufacturing Processes |
| 23CV201 | Basic Civil Engineering | Core | 4 | Building Materials, Building Construction Techniques, Surveying and Leveling, Transportation Engineering, Environmental Engineering Concepts |
| 23HS201 | Indian Constitution | Core | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Governments, Constitutional Amendments, Emergency Provisions |
| 23SD201 | Universal Human Values | Skill Development | 2 | Introduction to Value Education, Harmony in the Individual, Harmony in the Family, Harmony in Society, Harmony in Nature and Existence |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AI301 | Data Structures | Core | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Hashing |
| 23AI302 | Digital Logic Design | Core | 4 | Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Registers and Counters, Memory and Programmable Logic |
| 23AI303 | Object Oriented Programming with Java | Core | 4 | OOP Concepts and Principles, Classes, Objects, and Methods, Inheritance and Polymorphism, Exception Handling and Multithreading, Generics and Collections Framework |
| 23AI304 | Discrete Mathematics | Core | 3 | Set Theory and Relations, Logic and Proof Techniques, Functions and Sequences, Graph Theory Fundamentals, Trees and Recurrence Relations |
| 23AI305 | Database Management Systems | Core | 4 | Introduction to DBMS and Data Models, Relational Model and Algebra, Structured Query Language (SQL), Database Design and Normalization, Transaction Management and Concurrency Control |
| 23AI306 | Environmental Studies | Audit (Mandatory Non-Credit) | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources and Conservation, Environmental Ethics and Laws, Climate Change and Sustainable Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AI401 | Analysis and Design of Algorithms | Core | 4 | Algorithm Analysis and Complexity, Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Graph Algorithms and Backtracking |
| 23AI402 | Operating Systems | Core | 4 | OS Introduction and Structure, Process Management and Scheduling, CPU Scheduling Algorithms, Memory Management Techniques, File Systems and I/O Systems |
| 23AI403 | Computer Organization and Architecture | Core | 4 | Basic Computer Organization, Data Representation and Arithmetic, Central Processing Unit Design, Memory Hierarchy and Cache, Input/Output Organization |
| 23AI404 | Python Programming | Core | 4 | Python Fundamentals and Data Types, Control Flow and Functions, Data Structures in Python (Lists, Tuples, Dictionaries), Object-Oriented Programming in Python, File Handling and Exception Handling |
| 23AI405 | Microcontroller | Core | 3 | Introduction to Microcontrollers, 8051 Architecture and Instruction Set, Timer, Counter and Serial Communication, Interrupts and Peripheral Interfacing, Embedded Systems Concepts |
| 23HS401 | Technical Communication | Skill Development | 2 | Principles of Technical Writing, Report and Proposal Writing, Presentation Skills, Group Discussion Strategies, Interview Preparation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AI501 | Theory of Computation | Core | 3 | Finite Automata and Regular Languages, Regular Expressions and Grammars, Context-Free Grammars and Pushdown Automata, Turing Machines, Undecidability and Complexity Classes |
| 23AI502 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Ensemble Methods and Boosting, Model Evaluation and Hyperparameter Tuning |
| 23AI503 | Artificial Intelligence | Core | 4 | Introduction to AI and Intelligent Agents, Problem Solving through Search, Knowledge Representation and Reasoning, Planning and Uncertainty, Machine Learning in AI |
| 23AI504 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical Layer and Data Link Layer, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| 23AIE511 | Natural Language Processing | Elective | 3 | NLP Fundamentals and Text Preprocessing, Language Models and N-grams, Part-of-Speech Tagging and Parsing, Machine Translation, Sentiment Analysis and Text Classification |
| 23AIE512 | Computer Vision | Elective | 3 | Image Formation and Perception, Image Processing Techniques, Feature Detection and Extraction, Image Segmentation and Object Recognition, Motion Analysis and Tracking |
| 23AIE513 | Deep Learning | Elective | 3 | Neural Network Basics, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and Generative Models, Deep Learning Frameworks (TensorFlow, PyTorch) |
| 23AIE514 | Reinforcement Learning | Elective | 3 | Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Policy Gradient Methods |
| 23SD501 | Intellectual Property Rights & Cyber Law | Skill Development | 2 | Introduction to Intellectual Property Rights, Patents, Trademarks, and Copyrights, Industrial Designs and Geographical Indications, Basics of Cyber Law and IT Act, Digital Signatures and Cybercrimes |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AI601 | Data Mining | Core | 4 | Introduction to Data Mining, Data Preprocessing and Exploration, Association Rule Mining, Classification Techniques, Clustering Algorithms |
| 23AI602 | Computer Graphics | Core | 4 | Graphics Systems and Displays, Output Primitives (Lines, Circles), 2D Transformations and Viewing, 3D Transformations and Projections, Visible Surface Detection and Shading |
| 23AI603 | Web Technologies | Core | 4 | HTML5 and CSS3, JavaScript and DOM, Web Servers and Web Hosting, Server-Side Scripting (PHP/ASP.NET), Database Connectivity for Web |
| 23AIE611 | Big Data Analytics | Elective | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming Model, Spark and its Components, NoSQL Databases |
| 23AIE612 | Robotics | Elective | 3 | Robot Kinematics, Robot Dynamics, Sensors and Actuators, Robot Control Architectures, AI in Robotics and Path Planning |
| 23AIE613 | Quantum Computing | Elective | 3 | Quantum Mechanics Fundamentals, Qubits and Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography, Quantum Machine Learning |
| 23AIE614 | Edge AI | Elective | 3 | Edge Computing Concepts, AI Models for Edge Devices, Hardware for Edge AI, Deployment of Edge AI Solutions, Applications of Edge AI |
| 23AIL601 | Mini Project | Project | 2 | Problem Definition and Literature Survey, Project Design and Methodology, Implementation and Testing, Result Analysis and Discussion, Report Writing and Presentation |
| 23SD601 | Professional Ethics & Social Responsibility | Skill Development | 2 | Ethical Theories and Dilemmas, Professionalism and Codes of Conduct, Corporate Social Responsibility, Cyber Ethics and Data Privacy, Environmental Ethics and Sustainability |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AI701 | Cloud Computing | Core | 4 | Introduction to Cloud Computing, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Cloud Security and Management |
| 23AI702 | Software Engineering | Core | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing and Quality Assurance, Software Project Management |
| 23AIE711 | Human Computer Interaction | Elective | 3 | HCI Principles and Paradigms, Usability Engineering, User Interface Design and Evaluation, Cognition and Interaction, Future of HCI |
| 23AIE712 | Fuzzy Logic | Elective | 3 | Fuzzy Sets and Operations, Fuzzy Relations, Fuzzy Logic Systems, Fuzzy Control, Applications of Fuzzy Logic |
| 23AIE713 | Swarm Intelligence | Elective | 3 | Nature-Inspired Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Genetic Algorithms, Applications of Swarm Intelligence |
| 23AIE714 | Game Theory | Elective | 3 | Strategic Form Games, Extensive Form Games, Nash Equilibrium, Cooperative Games, Applications in AI and Economics |
| 23AIP701 | Major Project Phase I | Project | 4 | Project Proposal Development, Comprehensive Literature Survey, System Design and Architecture, Feasibility Study and Planning, Initial Implementation and Prototype |
| 23SD701 | Entrepreneurship and Startup Ecosystem | Skill Development | 3 | Entrepreneurial Mindset, Business Plan Development, Startup Funding and Valuation, Legal Aspects for Startups, Marketing and Scaling Strategies |
| 23AIINT1 | Internship | Internship | 3 | Industry Exposure and Experience, Practical Skill Application, Problem-Solving in Real-World Scenarios, Professional Development and Networking, Internship Report Submission |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23AIE811 | Business Intelligence | Elective | 3 | Business Intelligence Concepts, Data Warehousing and OLAP, Data Mining for Business Intelligence, Data Visualization and Dashboards, Decision Support Systems |
| 23AIE812 | Blockchain Technology | Elective | 3 | Blockchain Fundamentals, Cryptocurrency and Bitcoin, Distributed Ledger Technologies, Smart Contracts and Ethereum, Blockchain Applications |
| 23AIE813 | Explainable AI | Elective | 3 | Interpretability vs Explainability, XAI Techniques and Methods, Model-Agnostic Interpretability, Local and Global Explanations, Ethical AI and Trustworthiness |
| 23AIE814 | Data Privacy and Security | Elective | 3 | Data Security Fundamentals, Privacy-Preserving Technologies, Cryptography and Anonymization, Network and Application Security, Data Governance and Compliance |
| 23AIP801 | Major Project Phase II | Project | 9 | Advanced Implementation and Development, Comprehensive Testing and Validation, Performance Analysis and Optimization, Detailed Documentation and Report, Project Presentation and Viva Voce |
