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B-E in Artificial Intelligence Machine Learning at PES Institute of Technology and Management
Shivamogga, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at PES Institute of Technology and Management Shivamogga?
This Artificial Intelligence & Machine Learning (AIML) program at PES Institute of Technology and Management, Shivamogga, focuses on equipping students with a robust foundation in cutting-edge AI and ML technologies. It prepares graduates to meet the rapidly expanding demand for skilled professionals in India''''s booming technology sector. The program emphasizes both theoretical knowledge and practical application, ensuring students are ready for real-world industry challenges and contribute to innovative solutions.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics, logical reasoning, and a keen interest in technology and problem-solving. It also caters to individuals seeking to enter the dynamic fields of AI, data science, and machine learning. Students with prior programming exposure or a desire to specialize in intelligent systems and data-driven decision making will find this course particularly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths as AI Engineers, Machine Learning Scientists, Data Analysts, NLP Specialists, and Computer Vision Engineers. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more (INR 10-30+ LPA). The curriculum aligns with industry-recognized certifications, facilitating growth trajectories in both Indian startups and multinational corporations operating within India.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Dedicate significant time to thoroughly understand fundamental programming concepts in C/Java and build strong mathematical skills, especially in calculus, linear algebra, and discrete mathematics. These form the bedrock for all advanced AI/ML topics.
Tools & Resources
HackerRank, LeetCode, Khan Academy (for Math), NPTEL courses on basic programming and mathematics, GeeksforGeeks
Career Connection
Strong fundamentals in these areas are critical for clearing initial technical rounds in placements and for understanding complex algorithms in later semesters.
Engage in Peer Learning and Group Projects- (Semester 1-2)
Form study groups, actively participate in discussions, and collaborate on small programming assignments. This improves problem-solving abilities, communication skills, and exposes students to diverse approaches to challenges, mirroring real-world team environments.
Tools & Resources
Discord/WhatsApp groups, GitHub for collaborative coding, College library resources
Career Connection
Enhances teamwork and communication skills, which are highly valued by recruiters for internship and full-time roles, fostering a supportive learning environment.
Explore AI/ML Beyond Curriculum (Basics)- (Semester 1-2)
Start watching introductory videos, reading popular science articles about AI, and experimenting with simple Python scripts. This early exposure builds interest and provides a contextual understanding for subjects introduced in later semesters.
Tools & Resources
Coursera/edX introductory ML courses, YouTube channels like ''''3Blue1Brown'''', Kaggle for beginner datasets
Career Connection
Develops a foundational intuition for AI/ML concepts, making advanced topics easier to grasp and showcasing proactive learning to potential employers.
Intermediate Stage
Build a Strong Portfolio with Practical Projects- (Semester 3-5)
Beyond lab assignments, undertake self-initiated projects in areas like data analysis, basic machine learning model implementation, or small AI applications. Focus on using real-world datasets and documenting your code properly.
Tools & Resources
Kaggle competitions, GitHub for project hosting, Python with libraries like scikit-learn, pandas, numpy
Career Connection
A robust project portfolio demonstrates practical skills and problem-solving abilities, significantly boosting internship and job prospects. It provides concrete examples for interviews.
Participate in Coding Contests and Hackathons- (Semester 3-5)
Regularly engage in competitive programming platforms and participate in college-level or regional hackathons. This sharpens algorithmic thinking, coding efficiency, and teaches rapid prototyping under pressure.
Tools & Resources
CodeChef, HackerEarth, TopCoder, College hackathon events
Career Connection
Develops critical thinking, problem-solving under time constraints, and competitive spirit, which are highly sought after by product-based companies.
Seek Early Industry Exposure through Workshops and Internships- (Semester 4-6 (earlier the better for internships))
Attend industry workshops, guest lectures, and try to secure short-term internships or virtual experiences. This provides insights into industry trends, tools, and professional work environments, preparing for future roles.
Tools & Resources
LinkedIn for networking, Internshala for internship search, Industry meetups in Bengaluru/Mysuru
Career Connection
Gaining early practical exposure helps in understanding industry expectations, building a professional network, and securing better long-term internships and placements.
Advanced Stage
Specialize and Contribute to Research/Advanced Projects- (Semester 6-8)
Identify a niche area within AIML (e.g., NLP, Computer Vision, Reinforcement Learning) and delve deeper. Work on advanced projects, contribute to open-source initiatives, or assist faculty with research papers. This builds deep expertise.
Tools & Resources
TensorFlow/PyTorch, Hugging Face, Academic papers (arXiv), University research labs
Career Connection
Specialization makes you a desirable candidate for targeted roles. Research experience is invaluable for R&D positions or higher studies (M.Tech/Ph.D.).
Intensive Placement Preparation and Mock Interviews- (Semester 7-8)
Focus on company-specific preparation, including data structures and algorithms, core AIML concepts, and behavioral interview questions. Participate in mock interviews to refine communication and problem-solving under pressure.
Tools & Resources
Interviewer.ai, Pramp, Glassdoor (for company interview experiences), College placement cell workshops
Career Connection
Directly impacts success in securing high-quality placements. Polished interview skills are crucial for converting opportunities.
Network Actively and Build Professional Presence- (Semester 6-8)
Attend industry conferences, connect with professionals on platforms like LinkedIn, and maintain an updated online presence (e.g., GitHub, personal website). This opens doors to mentorship, job opportunities, and staying current with industry trends.
Tools & Resources
LinkedIn, GitHub, Medium (for writing technical blogs), Local tech meetups
Career Connection
Expands career opportunities beyond direct campus placements, leading to better roles and long-term professional growth in the competitive Indian tech landscape.
Program Structure and Curriculum
Eligibility:
- As per Visvesvaraya Technological University (VTU) norms: 10+2 with Physics, Mathematics, and one of Chemistry/Biology/Biotechnology/Technical Vocational Subject with minimum aggregate marks.
Duration: 4 years / 8 semesters
Credits: 150 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT101 | Engineering Mathematics-I | Core | 3 | Differential Calculus, Integral Calculus, Vector Calculus, Multivariable Calculus, First-Order Differential Equations |
| BT102 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines, Electrical Safety |
| BT103 | Computer Programming | Core | 3 | C Programming Fundamentals, Data Types and Operators, Control Flow, Functions, Arrays and Pointers, Structures and Unions |
| BT104 | Elements of Civil Engineering | Core | 3 | Surveying and Leveling, Building Materials, Basic Structural Elements, Water Resources, Transportation Engineering, Environmental Engineering |
| BT105 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Projection of Solids, Sectional Views, Development of Surfaces |
| BT106 | Basic Electrical Engineering Lab | Lab | 1 | Ohm''''s Law Verification, Kirchhoff''''s Laws, Thevenin''''s and Norton''''s Theorem, Resonance in AC Circuits, Transformer Characteristics |
| BT107 | Computer Programming Lab | Lab | 1 | C Program Structure, Conditional Statements, Looping Constructs, Function Implementation, Array and String Operations |
| BT108 | Professional Communication | Core | 3 | Grammar and Vocabulary, Verbal and Non-verbal Communication, Report Writing, Presentation Skills, Group Discussions and Interviews |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT201 | Engineering Mathematics-II | Core | 3 | Linear Algebra, Eigenvalues and Eigenvectors, Complex Numbers, Fourier Series, Partial Differential Equations |
| BT202 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion and its Control, Polymer Chemistry, Water Technology, Fuel Chemistry, Energy Storage Devices |
| BT203 | Mechanics of Materials | Core | 3 | Stress and Strain, Elastic Constants, Shear Force and Bending Moment, Torsion of Circular Shafts, Columns and Struts |
| BT204 | Computer Organization & Architecture | Core | 3 | Digital Logic Circuits, Basic Computer Functions, CPU Organization, Memory Organization, Input/Output Organization, Pipelining |
| BT205 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Searching Algorithms, Sorting Algorithms, Hashing |
| BT206 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Conductometric Titration, pH Metry, Viscosity Determination, Colorimetry |
| BT207 | Data Structures Lab | Lab | 1 | Array Implementation, Linked List Operations, Stack and Queue Applications, Tree Traversals, Graph Algorithms |
| BT208 | Constitution of India and Professional Ethics | Core | 3 | Indian Constitution Features, Fundamental Rights, Directive Principles, Engineering Ethics, Professional Responsibility, Cyber Law |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM301 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations, Algebraic Structures |
| AM302 | Object Oriented Programming with Java | Core | 3 | OOP Concepts, Java Fundamentals, Classes and Objects, Inheritance and Polymorphism, Exception Handling, Collections Framework |
| AM303 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| AM304 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Normalization, Transaction Management, Concurrency Control, Database Security |
| AM305 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| AM306 | Java Laboratory | Lab | 1 | Class and Object Implementation, Inheritance and Interface, Exception Handling, Multithreading, JDBC Connectivity |
| AM307 | DBMS Laboratory | Lab | 1 | SQL DDL and DML, Joins and Subqueries, Views and Stored Procedures, Triggers, Database Connectivity |
| AM308 | Research Methodology and IPR | Core | 3 | Research Problem Formulation, Data Collection Methods, Report Writing, Patents and Copyrights, Trademarks and Industrial Designs, IPR in Digital Age |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM401 | Probability & Statistics | Core | 3 | Probability Distributions, Random Variables, Hypothesis Testing, Regression Analysis, Correlation, Statistical Inference |
| AM402 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| AM403 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| AM404 | Machine Learning | Core | 3 | Introduction to ML, Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Model Evaluation, Ensemble Methods |
| AM405 | Artificial Intelligence | Core | 3 | AI Foundations, Problem Solving Agents, Search Algorithms, Knowledge Representation, Planning, Uncertainty and Probabilistic Reasoning |
| AM406 | Machine Learning Lab | Lab | 1 | Data Preprocessing, Linear Regression Implementation, Classification Algorithms, Clustering Techniques, Model Evaluation Metrics |
| AM407 | AI Lab | Lab | 1 | Heuristic Search Algorithms, Game Playing AI, Constraint Satisfaction Problems, Knowledge Representation with Prolog, Planning Algorithms |
| AM408 | Environmental Studies | Core | 3 | Ecosystems, Biodiversity Conservation, Environmental Pollution, Solid Waste Management, Sustainable Development, Environmental Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM501 | Deep Learning | Core | 3 | Neural Network Architecture, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Generative Adversarial Networks (GANs) |
| AM502 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Syntactic and Semantic Analysis, Information Extraction, Machine Translation, Text Classification |
| AM503 | Data Mining and Warehousing | Core | 3 | Data Warehouse Architecture, OLAP Operations, Association Rule Mining, Classification and Prediction, Clustering Techniques, Anomaly Detection |
| AM504 | Professional Elective-I (e.g., Pattern Recognition) | Elective | 3 | Statistical Pattern Recognition, Syntactic Pattern Recognition, Clustering Algorithms, Feature Extraction, Classification Techniques, Applications of PR |
| AM505 | Open Elective-I (e.g., Introduction to Data Science) | Elective | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Statistical Inference, Machine Learning Concepts, Data Visualization |
| AM506 | Deep Learning Lab | Lab | 1 | Neural Network Implementation (TensorFlow/Keras), CNN for Image Classification, RNN for Sequence Data, Hyperparameter Tuning, Model Deployment |
| AM507 | Data Mining Lab | Lab | 1 | Data Preprocessing with Python, Association Rule Mining, Classification using Decision Trees, Clustering using K-Means, WEKA Tool Usage |
| AM508 | Technical Seminar | Project | 3 | Literature Survey, Technical Report Writing, Presentation Skills, Topic Selection in AI/ML, Research Gap Identification |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM601 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference Learning, Q-Learning and SARSA, Deep Reinforcement Learning |
| AM602 | Computer Vision | Core | 3 | Image Formation, Feature Detection, Image Segmentation, Object Recognition, Motion Estimation, Deep Learning for Vision |
| AM603 | Big Data Analytics | Core | 3 | Big Data Technologies, Hadoop Ecosystem, MapReduce, Spark, NoSQL Databases, Streaming Data Analysis |
| AM604 | Professional Elective-II (e.g., Explainable AI) | Elective | 3 | Interpretability vs Explainability, Local and Global Explanations, SHAP and LIME, Model-agnostic Methods, Explainable Deep Learning, Ethical Implications of XAI |
| AM605 | Open Elective-II (e.g., Data Visualization) | Elective | 3 | Principles of Visualization, Data Types and Visual Mapping, Graph and Network Visualization, Interactive Visualizations, Tools: Tableau, Power BI, D3.js, Storytelling with Data |
| AM606 | Reinforcement Learning Lab | Lab | 1 | OpenAI Gym Environment, Dynamic Programming Implementation, Q-Learning on Simple Grids, Deep Q-Networks (DQN), Policy Gradient Methods |
| AM607 | Computer Vision Lab | Lab | 1 | Image Processing with OpenCV, Feature Detection and Matching, Object Detection using YOLO/SSD, Image Segmentation, Face Recognition Systems |
| AM608 | Internship | Internship | 3 | Industry Problem Solving, Report Writing, Presentation, Teamwork, Professional Communication, Domain-specific Skill Application |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM701 | Professional Elective-III (e.g., AI in Gaming) | Elective | 3 | Game AI Architecture, Pathfinding Algorithms, Behavior Trees, Decision Making in Games, NPC Intelligence, Procedural Content Generation |
| AM702 | Open Elective-III (e.g., Entrepreneurship & Innovation) | Elective | 3 | Entrepreneurial Mindset, Business Model Canvas, Startup Funding, Marketing Strategies, Intellectual Property for Startups, Innovation Management |
| AM703 | Internship | Internship | 3 | Advanced Industry Project, Mentorship, Solution Design, Implementation Challenges, Project Documentation, Stakeholder Communication |
| AM704 | Project Work Phase - I | Project | 3 | Problem Identification, Literature Review, System Design, Methodology Selection, Preliminary Implementation, Project Report Writing |
| AM705 | Research Work | Core | 3 | Advanced Research Methodologies, Data Analysis Tools, Paper Writing, Ethical Research Practices, Interdisciplinary Research, Publication Strategies |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AM801 | Project Work Phase - II | Project | 3 | Project Implementation, Testing and Validation, Results Analysis, Optimization, Final Project Report, Viva-Voce Preparation |
| AM802 | Internship | Internship | 3 | Full-stack Project Development, Client Interaction, Deployment Strategies, Post-implementation Support, Agile Development Practices, Portfolio Building |
| AM803 | Professional Elective-IV (e.g., Cognitive Computing) | Elective | 3 | Cognitive Architectures, Natural Language Understanding, Machine Perception, Reasoning Systems, Cognitive Robotics, Affective Computing |
| AM804 | Open Elective-IV (e.g., Financial Engineering) | Elective | 3 | Financial Markets, Derivatives Pricing, Risk Management, Quantitative Finance, Algorithmic Trading, Financial Modeling |
| AM805 | Technical Seminar | Project | 3 | Emerging Technologies in AI/ML, Advanced Literature Review, Research Presentation, Critical Analysis, Future Scope Identification, Ethical Considerations in AI |
