.png&w=1920&q=75)
BACHELOR-OF-ENGINEERING in Artificial Intelligence And Machine Learning at Bapuji Institute of Engineering & Technology
Davangere, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence and Machine Learning at Bapuji Institute of Engineering & Technology Davangere?
This Artificial Intelligence and Machine Learning (AI&ML) program at Bapuji Institute of Engineering and Technology focuses on equipping students with a robust foundation in cutting-edge AI and ML technologies. In the burgeoning Indian tech landscape, this specialization is crucial for developing intelligent systems, driving innovation in sectors like healthcare, finance, and e-commerce. The program''''s blend of theoretical knowledge and practical application addresses the high industry demand for skilled AI professionals.
Who Should Apply?
This program is ideal for aspiring engineers eager to delve into advanced computing and data-driven intelligence. It caters to fresh graduates seeking entry into the dynamic fields of AI, data science, and machine learning, and working professionals looking to upskill in areas like deep learning or natural language processing. A strong aptitude for mathematics, logical reasoning, and an interest in problem-solving using computational methods are beneficial prerequisites.
Why Choose This Course?
Graduates of this program can expect promising career paths as AI engineers, Machine Learning specialists, Data Scientists, or Robotics engineers within India''''s leading tech companies and startups. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals commanding significantly higher packages. The program fosters critical thinking and innovation, preparing students for leadership roles and potential entrepreneurial ventures in the rapidly expanding AI ecosystem.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice core programming concepts in C and Python, focusing on data structures and algorithms. Utilize online platforms for coding challenges and learn to debug efficiently, forming the bedrock for AI/ML application development.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, VS Code
Career Connection
Strong programming skills are fundamental for technical interviews and efficient implementation of machine learning algorithms, critical for entry-level AI/ML roles.
Build a Strong Mathematical Base- (Semester 1-2)
Pay close attention to Multivariable Calculus, Linear Algebra, Probability, and Statistics. Understand the underlying mathematical principles thoroughly as they are indispensable for comprehending and building complex machine learning algorithms.
Tools & Resources
Khan Academy, NPTEL courses, reference textbooks (e.g., Gilbert Strang for Linear Algebra)
Career Connection
A solid mathematical understanding enables better comprehension of complex ML models, leading to effective model design, optimization, and troubleshooting in professional settings.
Engage in Peer Learning and Small Projects- (Semester 1-2)
Form study groups to discuss challenging topics and collaboratively solve problems. Start working on small, self-initiated projects like data manipulation scripts to apply learned concepts and build a foundational portfolio.
Tools & Resources
GitHub for code sharing, Google Meet for collaborative sessions, local IDEs, Kaggle introductory datasets
Career Connection
Teamwork and practical application skills are highly valued in industry. Early project experience demonstrates initiative, problem-solving abilities, and prepares you for collaborative engineering environments.
Intermediate Stage
Dive into Data Science & ML Frameworks- (Semester 3-5)
Gain hands-on experience with Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and begin exploring TensorFlow or PyTorch. Implement machine learning algorithms both from scratch and using these industry-standard libraries.
Tools & Resources
Kaggle, Coursera (e.g., ''''Applied Data Science with Python'''' specialization), Jupyter Notebooks, Google Colab
Career Connection
Proficiency in these tools and frameworks is essential for data scientist and machine learning engineer roles, directly impacting readiness for industry projects and advanced development tasks.
Undertake Mini-Projects and Internships- (Semester 3-5)
Actively seek out mini-projects, either self-driven or academic, focusing on real-world data problems. Secure at least one internship to gain initial industry exposure, understand project workflows, and apply academic knowledge in a professional environment.
Tools & Resources
LinkedIn for internship searches, university career services, project-based learning platforms, GitHub for project showcasing
Career Connection
Internships and relevant projects are crucial for building a strong portfolio, networking with professionals, and significantly enhancing your chances of securing placements in competitive companies.
Participate in Coding and AI/ML Competitions- (Semester 3-5)
Join online coding competitions and AI/ML hackathons regularly. This sharpens problem-solving skills under pressure, exposes you to diverse technical challenges, and fosters collaborative problem-solving, improving your competitive edge.
Tools & Resources
CodeChef, TopCoder, Kaggle competitions, university tech fests and hackathons
Career Connection
Success in competitions demonstrates advanced problem-solving, analytical skills, and often catches the eye of recruiters, opening doors to advanced technical roles and networking opportunities.
Advanced Stage
Specialize and Build a Strong Project Portfolio- (Semester 6-8)
Focus on a niche area within AI/ML (e.g., Deep Learning for Vision, NLP, Reinforcement Learning) and undertake a significant capstone project. Thoroughly document your projects on GitHub with clear explanations and demonstrations.
Tools & Resources
GitHub, Google Colab, Cloud platforms (AWS, Azure, GCP), specific research papers in your chosen niche
Career Connection
A specialized project portfolio showcases expertise in a particular domain, differentiating you from other candidates and aligning you with specific, high-demand job roles in the AI/ML industry.
Master Interview Preparation & Soft Skills- (Semester 6-8)
Dedicate extensive time to practicing technical interview questions, including data structures, algorithms, system design, and AI/ML specific concepts. Concurrently, develop strong communication, presentation, and teamwork skills for holistic professional readiness.
Tools & Resources
InterviewBit, LeetCode (mock interviews), company-specific interview guides, Toastmasters clubs, professional workshops
Career Connection
Excellent interview performance is critical for converting job opportunities into offers, while strong soft skills ensure professional success, leadership potential, and effective team collaboration.
Network Proactively and Explore Research- (Semester 6-8)
Attend industry seminars, workshops, and virtual conferences relevant to AI/ML. Connect with professionals and alumni on LinkedIn. Consider exploring cutting-edge research papers and contributing to open-source projects to stay updated and build connections.
Tools & Resources
LinkedIn, IEEE Xplore, arXiv, conference websites, university research groups
Career Connection
Networking opens doors to hidden job opportunities and mentorship, while research exposure fosters innovative thinking, valuable for R&D roles, academic pursuits, or entrepreneurial ventures in the AI sector.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 154 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MATS11 | Multivariable Calculus and Linear Algebra | Core | 4 | Partial Differentiation, Multiple Integrals, Vector Calculus, Matrices, Eigenvalues and Eigenvectors |
| 22ES12 | Engineering Physics | Core | 4 | Quantum Mechanics, Lasers, Optical Fibers, Material Science, Nanoscience |
| 22AIM13 | Introduction to AI & ML | Core | 3 | History of AI, AI Applications, Machine Learning Basics, Supervised Learning, Unsupervised Learning, Ethics in AI |
| 22EGDL14 | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, AutoCAD |
| 22PCD15 | Programming for Problem Solving | Core | 3 | C Programming, Data Types, Control Structures, Functions, Arrays, Pointers |
| 22ESL16 | Engineering Physics Lab | Lab | 1 | Experiments on Lasers, Optical fibers, Semiconductor devices, Logic gates, Material properties |
| 22PCDL17 | Programming for Problem Solving Lab | Lab | 1 | C Programming exercises, Debugging techniques, File I/O operations, Basic data structures, Algorithm implementation |
| 22CIV18 / 22CHE18 | Environmental Science / Constitution of India and Professional Ethics | Mandatory Non-credit | 0 | Environmental Pollution, Natural Resources, Biodiversity, Indian Constitution, Professional Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MATS21 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Sampling Theory, Hypothesis Testing |
| 22ES22 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Water Technology, Fuels, Polymers, Nanomaterials |
| 22AIM23 | Data Structures and Algorithms | Core | 3 | Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| 22BEG24 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Motors, Power Systems |
| 22AIM25 | Object Oriented Programming with Python | Core | 3 | Python Fundamentals, OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling |
| 22ESL26 | Engineering Chemistry Lab | Lab | 1 | Volumetric analysis, pH metry, Conductometry, Colorimetry, Material synthesis |
| 22AIML27 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Stacks, Queues, Linked Lists, Trees, Sorting Algorithms, Searching Algorithms |
| 22AIPL28 | Object Oriented Programming Lab with Python | Lab | 1 | Python OOP exercises, File operations, Database connectivity, GUI programming, Web scraping basics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM31 | Discrete Mathematics | Core | 4 | Logic, Set Theory, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
| 22AIM32 | Computer Organization & Architecture | Core | 3 | Basic Computer Functions, CPU Organization, Memory System, I/O Organization, Pipelining |
| 22AIM33 | Database Management Systems | Core | 3 | DBMS Architecture, ER Model, Relational Model, SQL, Normalization, Transaction Management |
| 22AIM34 | Design and Analysis of Algorithms | Core | 3 | Algorithm Design Techniques, Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming |
| 22AIM35 | Python Programming for Data Science | Core | 3 | NumPy, Pandas, Matplotlib, Data Preprocessing, Data Visualization, Statistical Analysis |
| 22AIML36 | DBMS Lab with Mini Project | Lab | 1 | SQL queries, Database design, Transaction management, Mini-project implementation, Data manipulation |
| 22AIML37 | Python Programming for Data Science Lab | Lab | 1 | Data loading and cleaning, Data transformation, Exploratory Data Analysis, Data visualization using Python libraries, Basic statistical computations |
| 22AIE38 | Internship/Skill Development Activity | Internship | 1 | Professional skill enhancement, Industry exposure, Report writing, Presentation skills, Project documentation |
| 22AIM39 | Universal Human Values | Mandatory Non-credit | 0 | Human values, Ethics and morality, Harmony in society, Professionalism, Social responsibility |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM41 | Operating Systems | Core | 3 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Systems |
| 22AIM42 | Artificial Intelligence | Core | 3 | Intelligent Agents, Search Algorithms, Game Playing, Knowledge Representation, Logical Reasoning, Planning |
| 22AIM43 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Model Evaluation, Deep Learning Basics |
| 22AIM44 | Computer Networks | Core | 3 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 22AIM45 | Web Technologies | Core | 3 | HTML, CSS, JavaScript, Web Servers, Client-Server Architecture, AJAX, PHP/Node.js basics |
| 22AIML46 | Artificial Intelligence Lab with Mini Project | Lab | 1 | AI search algorithms, Logic programming, Expert systems, Mini-project development, Problem-solving using AI techniques |
| 22AIML47 | Machine Learning Lab | Lab | 1 | Implementation of ML algorithms, Data preprocessing, Model training and testing, Evaluation metrics, Hyperparameter tuning |
| 22AIE48 | Internship/Skill Development Activity | Internship | 1 | Practical skill enhancement, Industry problem exposure, Teamwork and communication, Project report submission, Presentation skills |
| 22AIM49 | Ability Enhancement Course (AEC) | Mandatory Non-credit | 0 | Communication Skills, Critical Thinking, Quantitative Aptitude, Soft Skills, Professional Etiquette |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM51 | Deep Learning | Core | 3 | Neural Networks, CNNs, RNNs, LSTMs, Backpropagation, Optimization, Transfer Learning |
| 22AIM52 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop, MapReduce, HDFS, Spark, NoSQL Databases |
| 22AIM53 | Natural Language Processing | Core | 3 | Text Preprocessing, N-grams, Word Embeddings, POS Tagging, Named Entity Recognition, Sentiment Analysis |
| 22AIMPE54X | Professional Elective - I | Elective | 3 | Cloud Computing (Cloud Models, Virtualization, AWS/Azure Basics), Computer Vision (Image Processing, Feature Extraction, Object Detection), Reinforcement Learning (MDPs, Q-learning, Policy Gradients), Data Warehousing & Data Mining (OLAP, Association Rules, Classification) |
| 22AIMOE55X | Open Elective - I | Elective | 3 | |
| 22AIML56 | Deep Learning Lab | Lab | 1 | Implementation of CNNs, RNNs, LSTMs, Transfer learning, Frameworks (TensorFlow/PyTorch) |
| 22AIML57 | Big Data Analytics Lab | Lab | 1 | Hadoop/Spark implementation, Data processing, Querying with Hive/Pig, Data visualization, Distributed computing |
| 22AIM58 | Project Work Phase - I | Project | 1 | Problem identification, Literature survey, System design, Feasibility study, Initial implementation |
| 22AIM59 | Internship | Internship | 2 | Industry work experience, Application of theoretical knowledge, Professional skill development, Project implementation, Technical report writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM61 | Advanced Machine Learning | Core | 3 | Ensemble Methods, Support Vector Machines, Kernel Methods, Dimensionality Reduction, Bias-Variance Tradeoff, Bayesian Learning |
| 22AIM62 | AI in Robotics | Core | 3 | Robot Kinematics, Sensors and Actuators, Motion Planning, Robot Vision, Human-Robot Interaction |
| 22AIM63 | Ethics in AI | Core | 3 | Ethical AI Principles, Bias in AI, Data Privacy, Explainable AI, Societal Impact, AI Regulations |
| 22AIMPE64X | Professional Elective - II | Elective | 3 | Internet of Things (IoT Architecture, Sensors, Protocols), Blockchain Technology (Cryptography, Distributed Ledgers, Smart Contracts), Computer Graphics (Graphics Pipeline, Transformations, Projections), Optimization Techniques (Linear Programming, Genetic Algorithms) |
| 22AIMOE65X | Open Elective - II | Elective | 3 | |
| 22AIML66 | Advanced Machine Learning Lab | Lab | 1 | Implementation of ensemble methods, SVMs, Dimensionality reduction techniques, Hyperparameter tuning, Model comparison |
| 22AIML67 | AI in Robotics Lab | Lab | 1 | Robot programming, Sensor integration, Path planning algorithms, Vision-based navigation, Robot control |
| 22AIM68 | Project Work Phase - II | Project | 1 | Detailed design and implementation, Module development, Testing and debugging, Mid-term project review, Refinement of project scope |
| 22AIM69 | Internship | Internship | 2 | Advanced industry exposure, Complex problem-solving, Team collaboration, Deliverable submission, Professional presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM71 | AI for Cybersecurity | Core | 3 | Threat Detection, Malware Analysis, Anomaly Detection, Network Security, AI in Cryptography |
| 22AIMPE72X | Professional Elective - III | Elective | 3 | Generative AI (GANs, VAEs, Diffusion Models), Quantum Computing for AI (Quantum Gates, Qubits, Quantum ML), Edge AI (Edge Devices, TinyML, Federated Learning), Explainable AI (XAI) (Interpretability, LIME, SHAP) |
| 22AIMPE73X | Professional Elective - IV | Elective | 3 | Speech Processing (Speech Recognition, Text-to-Speech), Game AI (Pathfinding, Decision Trees, Agent Behavior), Digital Image Processing (Image Enhancement, Restoration, Segmentation), Human Computer Interaction (Usability, UX, Interaction Design) |
| 22AIM74 | Internship | Internship | 3 | In-depth industry project, Advanced technical skill application, Professional networking, Comprehensive report preparation, Final presentation |
| 22AIM75 | Project Work Phase - III | Project | 3 | Advanced implementation, Performance evaluation, Optimization techniques, Integration of modules, Documentation and testing |
| 22AIM76 | Research Methodology and IPR | Mandatory Non-credit | 0 | Research Design, Data Collection Methods, Statistical Analysis, Report Writing, Intellectual Property Rights (IPR) |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22AIM81 | Project Work Phase - IV | Project | 10 | Final project development, Comprehensive testing, Demonstration and presentation, Report submission, Addressing project challenges |
| 22AIM82 | Internship | Internship | 10 | Full-time industry experience, Contribution to real-world projects, Advanced skill application, Mentorship and professional growth, Career readiness |
