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B-TECH in Cse Artificial Intelligence Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University)
Guntur, Andhra Pradesh
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About the Specialization
What is CSE - Artificial Intelligence & Machine Learning at Koneru Lakshmaiah Education Foundation (Deemed to be University) Guntur?
This B.Tech in CSE - Artificial Intelligence & Machine Learning program at Koneru Lakshmaiah Deemed to be University focuses on equipping students with deep knowledge and practical skills in cutting-edge AI and ML technologies. With a robust curriculum designed to meet the dynamic needs of the Indian industry, this specialization prepares graduates for high-demand roles in AI development, data science, and intelligent system design. The program''''s blend of theoretical foundations and hands-on experience provides a strong differentiator in the competitive job market.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude in mathematics and programming, seeking entry into rapidly expanding fields like AI, ML, and Data Science. It also caters to working professionals aiming to upskill in advanced AI methodologies or career changers looking to transition into the AI industry. Candidates with a foundational understanding of computer science concepts and a passion for innovative problem-solving are particularly well-suited.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as AI Engineers, Machine Learning Scientists, Data Scientists, NLP Specialists, and Robotics Engineers in leading tech companies and startups. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning upwards of INR 15-30+ LPA, depending on skills and company. The program also prepares students for advanced studies and professional certifications in AI/ML, fostering significant growth trajectories in Indian and global tech firms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Data Structures- (Semester 1-2)
Dedicate significant time to mastering core programming concepts (Python/Java) and fundamental data structures and algorithms. Participate in coding challenges regularly on platforms like HackerRank and LeetCode to build problem-solving muscle and prepare for technical interviews early on.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on DSA
Career Connection
Strong DSA skills are non-negotiable for entry-level software development and AI/ML engineering roles, directly impacting selection in campus placements and competitive internships.
Build a Strong Mathematical Foundation- (Semester 1-3)
Focus on understanding Linear Algebra, Calculus, Probability, and Statistics thoroughly, as these are the backbone of AI and ML. Actively solve problems and relate theoretical concepts to potential AI/ML applications. Form study groups to discuss complex topics.
Tools & Resources
Khan Academy, MIT OpenCourseware (Mathematics), NPTEL courses
Career Connection
A solid mathematical foundation is critical for comprehending complex ML algorithms, enabling students to innovate and debug models effectively, which is essential for research and advanced AI/ML roles.
Engage in Early AI/ML Exploration & Projects- (Semester 1-2)
Utilize introductory AI/ML courses to explore basic concepts and build small projects. Leverage online tutorials and datasets to implement simple regression, classification, or clustering models. Document your code and learning journey on platforms like GitHub.
Tools & Resources
Kaggle (for datasets and notebooks), Coursera/Udemy for beginner ML courses, GitHub
Career Connection
Early exposure and practical experience provide a competitive edge. Mini-projects showcase initiative and build a foundational portfolio, appealing to recruiters for internships in AI/ML.
Intermediate Stage
Specialize in Core AI/ML Domains through Electives- (Semester 3-5)
Carefully select professional and open electives that align with specific interests within AI/ML (e.g., Deep Learning, NLP, Reinforcement Learning, Computer Vision). Delve deep into these areas beyond classroom content through self-study and specialized online courses.
Tools & Resources
Fast.ai, DeepLearning.ai courses, Google AI/ML resources
Career Connection
Specialization helps in targeting specific job roles (e.g., NLP Engineer, Computer Vision Scientist). Deep expertise in a niche area makes candidates highly desirable for specialized roles in startups and R&D divisions.
Participate in Hackathons and AI Competitions- (Semester 4-6)
Actively participate in university, national, and international hackathons and AI/ML competitions. These events provide intense practical exposure, teamwork experience, and opportunities to apply learned concepts to real-world problems under pressure.
Tools & Resources
Kaggle Competitions, Hackerearth, Devfolio, College hackathons
Career Connection
Winning or even participating in competitions demonstrates problem-solving abilities, teamwork, and practical skills. This experience is highly valued by employers and can lead to direct hiring or interviews.
Network and Seek Mentorship- (Semester 3-5)
Attend industry workshops, seminars, and guest lectures. Connect with alumni and industry professionals on LinkedIn. Seek mentorship from faculty or industry experts to gain insights into career paths, project guidance, and potential internship opportunities.
Tools & Resources
LinkedIn, Professional AI/ML communities (e.g., PyData India), Departmental industry connect events
Career Connection
Networking opens doors to internships, job referrals, and valuable career advice. Mentorship can provide strategic guidance, helping students navigate their academic and professional journey effectively.
Advanced Stage
Undertake Industry Internships and Major Projects- (Semester 6-7 (Internship), Semester 6-8 (Major Project))
Secure internships with reputable companies to gain hands-on experience in real-world AI/ML projects. Focus on developing robust, deployment-ready solutions. Leverage major projects to solve complex problems and showcase a comprehensive skill set in a specialized area.
Tools & Resources
Internshala, Naukri.com, Company career portals, Faculty research projects
Career Connection
Internships are often a direct pathway to pre-placement offers (PPOs) in India. Strong major projects serve as a capstone experience, demonstrating expertise and readiness for full-time roles.
Develop a Professional Portfolio and Resume- (Semester 7-8)
Curate a strong online portfolio (GitHub, personal website) showcasing all projects, code, and contributions. Tailor your resume to highlight AI/ML skills, projects, and relevant experiences, emphasizing impact and measurable outcomes. Practice mock interviews for technical and behavioral rounds.
Tools & Resources
GitHub, LinkedIn, Resume builders, Mock interview platforms
Career Connection
A well-crafted portfolio and resume are crucial for standing out in the Indian job market. Effective interview practice ensures confidence and articulate communication of skills to potential employers during campus placements.
Explore Entrepreneurial Ventures or Research Opportunities- (Semester 7-8)
For those inclined, explore the feasibility of an AI/ML startup idea or contribute to advanced research papers under faculty guidance. Engage with university innovation cells or incubators for startup support, or present research at conferences.
Tools & Resources
KLU Incubation Centre, Research labs, Journals (e.g., IEEE, ACM), Startup India initiatives
Career Connection
Entrepreneurial experience provides leadership and business acumen, while research contributions can lead to academic careers or highly specialized R&D roles. Both enhance marketability for diverse career paths beyond traditional placements.
Program Structure and Curriculum
Eligibility:
- 10+2 or equivalent with 60% aggregate in Physics, Chemistry, Mathematics (PCM).
Duration: 4 years / 8 semesters
Credits: 169 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS1001 | Linear Algebra and Calculus | Core (Basic Science) | 4 | Matrices and Eigenvalue Problems, Calculus of one variable, Multivariable Calculus, Vector Calculus, Ordinary Differential Equations |
| BS1002 | Engineering Physics | Core (Basic Science) | 4 | Quantum Mechanics, Solid State Physics, Semiconductor Physics, Lasers and Fiber Optics, Superconductivity and Dielectric materials |
| ES1001 | Programming for Problem Solving | Core (Engineering Science) | 4 | Introduction to Programming, Control Structures, Functions and Pointers, Structures and Unions, File Handling |
| ES1002 | Engineering Graphics | Core (Engineering Science) | 3 | Introduction to Engineering Graphics, Orthographic Projections, Projections of Solids, Section of Solids, Isometric Projections |
| HS1001 | Technical English and Communication Skills | Core (Humanities & Social Science) | 2 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Written Communication, Oral Communication |
| ES1003 | Programming for Problem Solving Lab | Lab (Engineering Science) | 1.5 | Basic Arithmetic Operations, Conditional Statements, Loop Control Statements, Arrays, Functions and Pointers, File Operations |
| BS1003 | Engineering Physics Lab | Lab (Basic Science) | 1.5 | Optical phenomena, Semiconductor characteristics, Magnetic field effects, Electrical circuits, Material properties |
| ES1004 | Engineering Graphics Lab | Lab (Engineering Science) | 1.5 | Orthographic projections using software, Isometric projections using software, Sectional views, Basic 2D drafting, Introduction to CAD tools |
| AI1001 | Artificial Intelligence and Machine Learning Fundamentals | Core (Professional Core Course) | 3 | Introduction to AI, Machine Learning Concepts, Data Preprocessing, Supervised Learning Basics, Unsupervised Learning Basics, Evaluation Metrics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS1004 | Probability and Statistics | Core (Basic Science) | 4 | Basic Probability, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| BS1005 | Engineering Chemistry | Core (Basic Science) | 4 | Water Technology, Electrochemistry, Corrosion, Fuel Technology, Polymer Chemistry, Advanced Engineering Materials |
| ES1005 | Basic Electrical Engineering | Core (Engineering Science) | 4 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Power Systems, Basic Electronics |
| CS1001 | Data Structures | Core (Professional Core Course) | 3 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees, Graphs, Hashing |
| HS1002 | Professional Ethics & Human Values | Core (Humanities & Social Science) | 2 | Human Values, Engineering Ethics, Ethical Theories, Moral Autonomy, Safety, Rights, and Responsibilities |
| BS1006 | Engineering Chemistry Lab | Lab (Basic Science) | 1.5 | Water quality analysis, pH meter, Conductometric titrations, Potentiometric titrations, Electroplating, Viscosity |
| ES1006 | Basic Electrical Engineering Lab | Lab (Engineering Science) | 1.5 | Ohm''''s Law, Kirchhoff''''s Laws, AC circuits, PN Junction diode, Transistor characteristics, Rectifiers |
| CS1002 | Data Structures Lab | Lab (Professional Core Course) | 1.5 | Array operations, Linked list implementations, Stack and Queue applications, Tree traversals, Graph algorithms |
| AI1002 | AI and ML Fundamentals Lab | Lab (Professional Core Course) | 1.5 | Python for AI/ML, Data manipulation with Pandas, Data visualization with Matplotlib, Scikit-learn basics, Regression models, Classification models |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS2001 | Discrete Mathematics and Logic | Core (Basic Science) | 4 | Mathematical Logic, Set Theory, Relations and Functions, Graph Theory, Algebraic Structures, Boolean Algebra |
| CS2001 | Object Oriented Programming through Java | Core (Professional Core Course) | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, Multithreading, GUI Programming |
| CS2002 | Computer Organization and Architecture | Core (Professional Core Course) | 3 | Basic Computer Organization, CPU Design, Memory Organization, I/O Organization, Pipelining, Parallel Processing |
| CS2003 | Operating Systems | Core (Professional Core Course) | 3 | Introduction to Operating Systems, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Management |
| AI2001 | Machine Learning Algorithms | Core (Professional Core Course) | 3 | Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, K-Means Clustering, Principal Component Analysis |
| CS2004 | Object Oriented Programming through Java Lab | Lab (Professional Core Course) | 1.5 | Classes and Objects implementation, Inheritance and Polymorphism examples, Exception handling, File I/O, JDBC connectivity |
| CS2005 | Operating Systems Lab | Lab (Professional Core Course) | 1.5 | Unix commands, Shell scripting, Process creation, Inter-process communication, CPU scheduling algorithms, Deadlock avoidance |
| AI2002 | Machine Learning Algorithms Lab | Lab (Professional Core Course) | 1.5 | Implementation of regression, Classification algorithms, Clustering algorithms, Model evaluation, Feature scaling, Hyperparameter tuning |
| AI2003 | Mini Project 1 | Project (Professional Core Course) | 2 | Problem identification, Literature survey, Design, Implementation, Testing, Project report writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BS2002 | Numerical Methods and Optimization | Core (Basic Science) | 4 | Solution of Equations, Interpolation, Numerical Differentiation and Integration, Optimization Techniques, Linear Programming |
| CS2006 | Database Management Systems | Core (Professional Core Course) | 3 | Introduction to DBMS, Relational Model, SQL, Normalization, Transaction Management, Concurrency Control |
| CS2007 | Design and Analysis of Algorithms | Core (Professional Core Course) | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| AI2004 | Deep Learning | Core (Professional Core Course) | 3 | Neural Networks, Perceptrons, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders |
| AI2005 | Natural Language Processing | Core (Professional Core Course) | 3 | Text Preprocessing, N-grams, Part-of-Speech Tagging, Sentiment Analysis, Text Classification, Word Embeddings |
| CS2008 | Database Management Systems Lab | Lab (Professional Core Course) | 1.5 | SQL queries, Database design, Joins, Triggers, Views, Procedures, Report generation |
| AI2006 | Deep Learning Lab | Lab (Professional Core Course) | 1.5 | Building neural networks with Keras/TensorFlow, CNN implementation, RNN implementation, Hyperparameter tuning, Image classification, Text generation |
| AI2007 | Natural Language Processing Lab | Lab (Professional Core Course) | 1.5 | Tokenization, Stemming/Lemmatization, POS tagging, Text vectorization, Sentiment analysis with NLTK, Text summarization |
| AI2008 | Mini Project 2 | Project (Professional Core Course) | 2 | Advanced problem solving, System design, Prototype development, Performance evaluation, Technical documentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS3001 | Compiler Design | Core (Professional Core Course) | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Code Generation |
| CS3002 | Computer Networks | Core (Professional Core Course) | 3 | Network Topologies, OSI/TCP-IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| HS3001 | Soft Skills and Personality Development | Core (Humanities & Social Science) | 2 | Self-Awareness, Time Management, Goal Setting, Leadership Skills, Interview Skills, Group Discussion |
| AI3001 | Reinforcement Learning | Core (Professional Core Course) | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Q-Learning, Deep Reinforcement Learning |
| AI3102 | Computer Vision (Professional Elective I) | Elective (Professional Elective Course) | 3 | Image Formation, Image Processing Basics, Feature Detection, Image Segmentation, Object Recognition, Motion Analysis |
| CS3003 | Internet of Things (IoT) (Open Elective I) | Elective (Open Elective Course) | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols, IoT Platforms, Data Analytics in IoT, Security in IoT |
| CS3004 | Computer Networks Lab | Lab (Professional Core Course) | 1.5 | Network commands, Socket programming, Protocol implementation, Routing algorithms, Network traffic analysis, Wireshark usage |
| AI3002 | Reinforcement Learning Lab | Lab (Professional Core Course) | 1.5 | MDP implementation, Q-learning algorithms, SARSA algorithms, Policy gradient methods, OpenAI Gym environments, Deep Q-Networks |
| AI3003 | Mini Project 3 | Project (Professional Core Course) | 2 | Advanced AI/ML problem, Solution design, Prototyping, Evaluation, Technical report, Presentation skills |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS3005 | Cloud Computing | Core (Professional Core Course) | 3 | Cloud Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, Cloud Storage |
| AI3004 | Data Mining and Data Warehousing | Core (Professional Core Course) | 3 | Data Warehouse Architecture, OLAP, Data Preprocessing, Association Rules, Classification, Clustering, Outlier Detection |
| AI3106 | Generative AI (Professional Elective II) | Elective (Professional Elective Course) | 3 | Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, Transformer Architectures, Text-to-Image Generation, Large Language Models |
| CS3006 | Web Technologies (Open Elective II) | Elective (Open Elective Course) | 3 | HTML, CSS, JavaScript, Web Servers, Client-Server Architecture, Database Connectivity, Web Security |
| CS3007 | Cloud Computing Lab | Lab (Professional Core Course) | 1.5 | Virtual machine deployment, Cloud storage services, Serverless computing, Containerization (Docker), Cloud monitoring, AWS/Azure/GCP services |
| AI3005 | Data Mining and Data Warehousing Lab | Lab (Professional Core Course) | 1.5 | Data preprocessing tools, OLAP queries, Association rule mining, Classification algorithms, Clustering algorithms, Data visualization |
| AI3006 | Skill Development Course 1 (Advanced AI/ML) | Core (Professional Core Course) | 2 | Industry specific AI/ML tools, Advanced frameworks, Real-world case studies, Problem-solving methodologies, Project based learning |
| AI3007 | Major Project Phase I | Project | 4 | Project proposal, Extensive literature review, Problem definition, System architecture design, Preliminary implementation, Project planning |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS4001 | Constitution of India and Environmental Science | Core (Humanities & Social Science) | 2 | Indian Constitution, Fundamental Rights, Environmental Pollution, Sustainable Development, Natural Resources, Biodiversity |
| AI4102 | Explainable AI (XAI) (Professional Elective III) | Elective (Professional Elective Course) | 3 | Interpretability vs Explainability, Local and Global Explanations, SHAP and LIME, Counterfactual Explanations, Explainable Deep Learning, Ethical AI |
| AI4106 | AI for Cybersecurity (Professional Elective IV) | Elective (Professional Elective Course) | 3 | Cybersecurity Fundamentals, AI in Threat Detection, Malware Analysis, Anomaly Detection, Network Intrusion Detection, Secure AI |
| AI4001 | Internship | Internship | 6 | Industry exposure, Real-world project experience, Professional skill development, Problem-solving, Report writing |
| AI4002 | Skill Development Course 2 (Advanced ML/DL Frameworks) | Core (Professional Core Course) | 2 | TensorFlow 2.x, PyTorch, Hugging Face Transformers, MLOps tools, Deployment of AI models, Model optimization |
| AI4003 | Major Project Phase II | Project | 6 | Full system implementation, Testing and validation, Performance analysis, Documentation, Project demonstration, Final report |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI4110 | Ethical AI (Professional Elective V) | Elective (Professional Elective Course) | 3 | AI Ethics Principles, Bias in AI, Fairness in AI, Transparency and Accountability, Privacy Concerns, Societal Impact of AI |
| AI4116 | Deep Reinforcement Learning (Professional Elective VI) | Elective (Professional Elective Course) | 3 | Function Approximation, Deep Q-Networks (DQNs), Policy Gradient Methods, Actor-Critic Methods, Asynchronous Advantage Actor-Critic (A3C), Model-Based RL |
| AI4004 | Entrepreneurship | Core (Humanities & Social Science) | 2 | Entrepreneurial Mindset, Business Idea Generation, Market Research, Business Plan Development, Funding Strategies, Legal Aspects of Business |
| AI4005 | Project Work Viva Voce | Project | 6 | Project presentation, Viva voce examination, Project defense, Q&A on project, Reflective learning |
