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B-E in Artificial Intelligence And Machine Learning at Poojya Doddappa Appa College of Engineering
Kalaburagi, Karnataka
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About the Specialization
What is Artificial Intelligence and Machine Learning at Poojya Doddappa Appa College of Engineering Kalaburagi?
This Artificial Intelligence and Machine Learning program at Poojya Doddappa Appa College of Engineering focuses on developing robust AI systems and predictive models. It addresses the growing demand for skilled professionals in India''''s rapidly expanding technology sector, emphasizing practical applications and ethical considerations. The curriculum integrates core computer science with advanced AI/ML techniques.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into cutting-edge AI/ML roles and working professionals looking to upskill in data science. It attracts individuals with strong analytical and mathematical backgrounds, eager to explore intelligent systems. Career changers transitioning from other engineering disciplines into the data-driven industry will also find this specialization highly rewarding and accessible.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as AI Engineers, Machine Learning Scientists, Data Scientists, and NLP specialists. Entry-level salaries range from INR 4-8 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry demands for certifications in cloud AI platforms and offers growth trajectories in various Indian tech companies and startups.
Student Success Practices
Foundation Stage
Build a Strong Programming Base with Python and C- (Semester 1-2)
Dedicate consistent time to practice coding problems in Python and C on platforms like HackerRank and LeetCode. Focus on understanding data structures and basic algorithms thoroughly, as these are the building blocks for advanced AI/ML concepts. Engage in peer programming sessions to learn different problem-solving approaches.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Jupyter Notebooks
Career Connection
Strong programming skills are non-negotiable for any AI/ML role, enabling faster development and efficient implementation of models during internships and job roles.
Master Engineering Mathematics and Statistics- (Semester 1-2)
Understand the core mathematical concepts like linear algebra, calculus, probability, and statistics. These are fundamental to grasping the underlying principles of machine learning algorithms. Utilize online courses and textbooks to supplement classroom learning, focusing on conceptual clarity rather than rote memorization.
Tools & Resources
Khan Academy, MIT OpenCourseWare for Mathematics, NPTEL lectures
Career Connection
A solid mathematical foundation helps in understanding complex algorithms, debugging models, and innovating new solutions, critical for research and development roles.
Participate in Tech Clubs and Basic Coding Competitions- (Semester 1-2)
Join the college''''s coding or AI/ML club to collaborate on small projects, learn from seniors, and participate in intra-college coding competitions. This provides practical exposure beyond academics and helps in developing teamwork and problem-solving skills in a competitive environment.
Tools & Resources
College Tech Clubs, CodeChef, Coding Blocks
Career Connection
Active participation demonstrates initiative and practical skill application, enhancing your resume for early-stage internships and project-based learning opportunities.
Intermediate Stage
Undertake Machine Learning Mini-Projects- (Semester 3-5)
Apply theoretical knowledge by working on small-scale machine learning projects using real-world datasets from platforms like Kaggle. Focus on implementing various supervised and unsupervised learning algorithms from scratch or using libraries like Scikit-learn. Document your code and findings meticulously.
Tools & Resources
Kaggle, Google Colab, Scikit-learn documentation, GitHub
Career Connection
Building a portfolio of projects is essential for showcasing practical skills to potential employers and gaining hands-on experience in the ML development lifecycle.
Gain Exposure to AI/ML Frameworks and Libraries- (Semester 3-5)
Familiarize yourself with industry-standard AI/ML frameworks such as TensorFlow, Keras, and PyTorch. Complete online certification courses or tutorials offered by these platforms. Understanding these tools is crucial for scalable and efficient model deployment.
Tools & Resources
TensorFlow tutorials, PyTorch documentation, Coursera/edX courses on Deep Learning, NPTEL AI/ML courses
Career Connection
Proficiency in these frameworks is highly sought after by companies hiring for AI Engineer and Deep Learning Researcher roles.
Network with Industry Professionals and Attend Workshops- (Semester 3-5)
Attend industry workshops, seminars, and hackathons organized by the college or local tech communities. Connect with AI/ML professionals on LinkedIn, seek mentorship, and learn about current industry trends and challenges. This helps in career guidance and identifying potential internship opportunities.
Tools & Resources
LinkedIn, Meetup groups, Industry conferences (e.g., Data Science Congress)
Career Connection
Networking opens doors to internships, mentorship, and job referrals, giving you an edge in the competitive Indian tech job market.
Advanced Stage
Engage in a Capstone Project or Industry Internship- (Semester 6-8)
Undertake a significant final year project, ideally sponsored by an industry partner, focusing on a real-world AI/ML problem. Alternatively, secure an industry internship to gain practical experience in an organizational setting. This experience is invaluable for understanding business applications of AI.
Tools & Resources
College placement cell, Internshala, Company career pages, Research papers
Career Connection
A strong capstone project or internship experience is often the most critical factor for placements, demonstrating your ability to deliver solutions in a professional environment.
Specialize in a Niche AI/ML Domain- (Semester 6-8)
Beyond core AI/ML, choose a specialization area such as Natural Language Processing, Computer Vision, Reinforcement Learning, or Ethical AI, based on your interest and market demand. Deep dive into advanced topics, latest research papers, and specific tools related to your chosen niche.
Tools & Resources
arXiv, Google Scholar, Specialized online courses (e.g., Coursera''''s NLP specialization)
Career Connection
Specialized knowledge makes you a more valuable candidate for specific roles and provides a clear career trajectory in areas like NLP Engineer, Computer Vision Scientist, or AI Ethicist.
Prepare for Placements and Professional Development- (Semester 6-8)
Actively participate in campus placement drives, mock interviews, and resume building workshops. Practice aptitude tests and technical interview questions related to AI/ML, data structures, and algorithms. Develop strong communication and presentation skills to articulate your project work effectively.
Tools & Resources
Placement Cell, InterviewBit, GeeksforGeeks for interview preparation, LinkedIn Learning for soft skills
Career Connection
Thorough preparation ensures you confidently navigate the recruitment process, leading to successful placements in top-tier companies and startups across India.
Program Structure and Curriculum
Eligibility:
- Pass in 2nd PUC/12th Grade with Physics and Mathematics as compulsory subjects, along with Chemistry/Biotechnology/Biology/Electronics/Computer Science as an optional subject, obtained a minimum of 45% marks in aggregate (40% for SC/ST/OBC) in the optional subjects, and English as one of the languages. Must have appeared for Karnataka CET (KCET) or JEE Main.
Duration: 8 semesters / 4 years
Credits: 175 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MATS11 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Vector Algebra, Linear Algebra, Laplace Transforms |
| 22ELN12 | Basic Electronics | Core | 3 | Semiconductor Diodes, Bipolar Junction Transistors, Op-Amps, Digital Logic Gates, Communication Systems |
| 22ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Transformers, DC Machines, AC Machines |
| 22CPL14 | C Programming for Problem Solving | Core | 3 | C Language Basics, Control Structures, Functions, Arrays and Strings, Pointers |
| 22MECH15 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, Engines, Power Plants, Fluid Mechanics, Manufacturing Processes |
| 22EGH16 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, CAD Introduction |
| 22CPL17 | C Programming Lab | Lab | 1 | Conditional Statements, Looping Constructs, Function Implementation, Array Manipulation, Basic File Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MATS21 | Engineering Mathematics-II | Core | 4 | Multi-variable Calculus, Vector Calculus, Ordinary Differential Equations, Partial Differential Equations, Numerical Methods |
| 22CHE22 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion, Polymers, Fuels, Water Technology |
| 22PHT23 | Engineering Physics | Core | 3 | Quantum Mechanics, Laser Physics, Fiber Optics, Material Science, Nanotechnology |
| 22CPS24 | Python Programming for AI | Core | 3 | Python Basics, Data Structures in Python, Functions and Modules, Object-Oriented Python, File Handling |
| 22CIV25 | Elements of Civil Engineering | Core | 3 | Building Materials, Surveying, Fluid Mechanics Basics, Environmental Engineering, Transportation Engineering |
| 22ELN27 | Basic Electronics Lab | Lab | 1 | Diode Characteristics, Rectifiers, Transistor Amplifiers, Logic Gates Verification, Oscillators |
| 22PBL28 | Project Based Learning | Project | 2 | Problem Identification, Literature Survey, Design and Implementation, Testing and Evaluation, Report Writing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI31 | Data Structures and Applications | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 22AI32 | Discrete Mathematical Structures | Core | 4 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics |
| 22AI33 | Analog and Digital Electronics | Core | 4 | Semiconductor Devices, Operational Amplifiers, Combinational Logic, Sequential Logic, Data Converters |
| 22AI34 | Computer Organization and Architecture | Core | 4 | Basic Computer Functions, Instruction Sets, CPU Organization, Memory System, Input/Output Organization |
| 22AI35 | Python Programming for Machine Learning | Core | 3 | Numpy for Numerical Operations, Pandas for Data Manipulation, Matplotlib for Visualization, Scikit-learn Basics, Introduction to TensorFlow/Keras |
| 22AIL36 | Data Structures Lab | Lab | 1 | Linked List Operations, Stack and Queue Implementation, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Practice |
| 22AIL37 | Digital Electronics Lab | Lab | 1 | Logic Gate Implementation, Combinational Circuits Design, Sequential Circuits Design, Flip-Flops and Counters, Registers |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI41 | Design and Analysis of Algorithms | Core | 4 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 22AI42 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 22AI43 | Microcontrollers and Embedded Systems | Core | 4 | 8051 Microcontroller, ARM Processors, Interfacing Techniques, RTOS Concepts, Embedded System Design |
| 22AI44 | Probability and Statistics for AI | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| 22AI45 | Object-Oriented Programming with Java | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, Multithreading |
| 22AIL46 | Algorithms Lab | Lab | 1 | Implementation of Sorting, Graph Traversal, Knapsack Problem, Shortest Path Algorithms, Minimum Spanning Tree |
| 22AIL47 | Operating Systems Lab | Lab | 1 | Process Scheduling Algorithms, Deadlock Detection, Memory Allocation, File System Operations, Shell Programming |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI51 | Database Management Systems | Core | 4 | ER Modeling, Relational Algebra, SQL Queries, Normalization, Transaction Management |
| 22AI52 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 22AI53 | Artificial Intelligence | Core | 4 | Intelligent Agents, Search Algorithms, Knowledge Representation, Machine Learning Introduction, Expert Systems |
| 22AI54 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Feature Engineering |
| 22AI55X | Professional Elective - I (e.g., Cloud Computing) | Elective | 3 | Cloud Service Models, Cloud Deployment Models, Virtualization, Cloud Security, AWS/Azure Basics |
| 22AIL56 | DBMS Lab | Lab | 1 | SQL DDL/DML Commands, Joins and Subqueries, Stored Procedures, Triggers, Report Generation |
| 22AIL57 | Machine Learning Lab | Lab | 1 | Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Training and Testing, Data Preprocessing, Feature Selection |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI61 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, TensorFlow/PyTorch |
| 22AI62 | Natural Language Processing | Core | 4 | Text Preprocessing, Language Models, Text Classification, Sentiment Analysis, Machine Translation |
| 22AI63 | Big Data Analytics | Core | 4 | Hadoop Ecosystem, MapReduce, Spark Framework, Data Warehousing, NoSQL Databases |
| 22AI64X | Professional Elective - II (e.g., Computer Vision) | Elective | 3 | Image Processing Basics, Feature Detection, Object Recognition, Image Segmentation, Motion Analysis |
| 22AI65X | Open Elective - I (e.g., Web Technologies) | Elective | 3 | HTML/CSS/JavaScript, Client-Server Architecture, Frontend Frameworks, Backend Development, Database Integration |
| 22AIL66 | Deep Learning Lab | Lab | 1 | CNN Implementation, RNN for Sequence Data, Transfer Learning, Model Optimization, Hyperparameter Tuning |
| 22AIP67 | Mini Project | Project | 2 | Project Proposal, Requirement Analysis, System Design, Implementation and Testing, Project Report and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AI71 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| 22AI72 | Ethical AI and Trustworthy Systems | Core | 4 | AI Ethics Principles, Bias and Fairness in AI, AI Transparency, Privacy and Data Security, Accountability in AI |
| 22AI73X | Professional Elective - III (e.g., Robotics and AI) | Elective | 3 | Robot Kinematics, Robot Control, Robot Vision, Path Planning, Human-Robot Interaction |
| 22AI74X | Professional Elective - IV (e.g., Data Privacy and Security) | Elective | 3 | Cryptography Basics, Network Security, Web Security, Data Anonymization, GDPR and Regulations |
| 22AIP75 | Project Work Phase - I | Project | 4 | Problem Statement, Literature Survey, Methodology Design, Initial Implementation, Mid-term Review |
| 22AIINT76 | Internship / Technical Seminar | Internship/Seminar | 2 | Industry Exposure, Skill Application, Emerging Technologies, Research Presentation, Technical Report |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIP81 | Project Work Phase - II | Project | 10 | Advanced Implementation, Extensive Testing, Result Analysis, Final Report Writing, Project Defense |
| 22AIV82 | Technical Seminar / Viva Voce | Seminar/Viva | 4 | Technical Presentation Skills, Subject Matter Expertise, Research Communication, Problem-solving Discussion, Industry Trends |
