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B-TECH in Computer Science And Engineering Artificial Intelligence Machine Learning Ai Ml at Keshav Memorial Institute of Technology
Hyderabad, Telangana
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About the Specialization
What is Computer Science and Engineering - Artificial Intelligence & Machine Learning (AI&ML) at Keshav Memorial Institute of Technology Hyderabad?
This Computer Science and Engineering - Artificial Intelligence & Machine Learning (AI&ML) program at Keshav Memorial Institute of Technology focuses on equipping students with expertise in designing, developing, and deploying intelligent systems. With India''''s rapid digital transformation, there is a significant demand for AI&ML professionals across various sectors, making this specialization highly relevant and future-proof. The program emphasizes a blend of theoretical foundations and practical applications.
Who Should Apply?
This program is ideal for aspiring engineers eager to delve into cutting-edge technologies like machine learning, deep learning, and data science. It caters to fresh 10+2 graduates with a strong aptitude for mathematics and problem-solving. Working professionals looking to upskill in AI/ML for career advancement in the Indian tech industry, or career changers from related engineering fields, will also find this specialization highly beneficial.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including AI Engineer, Machine Learning Specialist, Data Scientist, Big Data Analyst, and Research Scientist. Entry-level salaries in India typically range from INR 5-8 LPA, while experienced professionals can command INR 15-30+ LPA, depending on skill and company. Growth trajectories are steep, with opportunities in startups, IT giants, and R&D divisions within India.
Student Success Practices
Foundation Stage
Strengthen Core Programming & Math Skills- (Semester 1-2)
Dedicate significant time to mastering C, Python, Data Structures, and algorithms. Concurrently, build a strong foundation in Linear Algebra, Calculus, Probability, and Statistics, as these are critical for AI/ML concepts. Practice problem-solving daily.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy for Math, NPTEL online courses
Career Connection
A solid foundation in these areas is crucial for acing technical interviews and understanding advanced AI/ML topics, directly impacting placement opportunities in top tech companies.
Participate in Coding Challenges & Tech Clubs- (Semester 1-2)
Actively engage in inter-college coding competitions and KMIT''''s own tech clubs. This helps in peer learning, competitive programming experience, and developing teamwork skills. Focus on solving problems collaboratively.
Tools & Resources
CodeChef, Spoj, KMIT''''s CSE Department clubs, Hackathons
Career Connection
Showcasing competitive programming achievements enhances resumes, demonstrates problem-solving ability, and builds a professional network, attracting recruiters from product-based companies.
Explore Open Source Projects & Version Control- (Semester 1-2)
Start contributing to simple open-source projects or creating personal projects. Learn Git and GitHub for version control. This provides practical exposure beyond classroom assignments and builds a portfolio.
Tools & Resources
GitHub, GitLab, VS Code, FreeCodeCamp
Career Connection
A well-maintained GitHub profile with real-world projects is highly valued by recruiters, indicating practical skills and self-learning capabilities for entry-level roles.
Intermediate Stage
Dive Deep into AI/ML Fundamentals with Projects- (Semester 3-5)
Focus on understanding the core concepts of Artificial Intelligence and Machine Learning (supervised, unsupervised learning, neural networks). Apply these concepts by building small-scale projects using Python libraries like Scikit-learn, Pandas, and NumPy.
Tools & Resources
Coursera/edX ML courses, Kaggle datasets, Jupyter Notebook, TensorFlow/PyTorch basics
Career Connection
Practical projects demonstrate your ability to apply theoretical knowledge, a key requirement for AI/ML intern and junior data scientist roles in Indian companies.
Seek Early Internship Opportunities- (Semester 4-5)
Look for summer or part-time internships, even unpaid ones, in AI/ML, data science, or related fields. This provides invaluable industry exposure, allows you to apply learned concepts, and helps build a professional network. Leverage KMIT''''s placement cell for leads.
Tools & Resources
Internshala, LinkedIn Jobs, KMIT Placement Cell
Career Connection
Internships are often a direct pathway to full-time employment in India and provide crucial experience that sets you apart from peers during placements.
Participate in Hackathons & Data Science Competitions- (Semester 3-5)
Engage in AI/ML-focused hackathons and data science competitions (e.g., Kaggle competitions). This hones your problem-solving skills under pressure and provides a platform to apply advanced techniques to real-world datasets.
Tools & Resources
Kaggle, Devfolio, Hackerearth, KMIT departmental events
Career Connection
Winning or performing well in such competitions adds significant weight to your resume, showcasing specialized skills and a competitive edge to potential employers.
Advanced Stage
Master Deep Learning & Specialized AI Domains- (Semester 6-8)
Focus on advanced topics like Deep Learning (CNNs, RNNs, Transformers), NLP, Computer Vision, or Reinforcement Learning based on your interest. Work on a major project or research paper in your chosen specialization, possibly under faculty guidance.
Tools & Resources
TensorFlow/PyTorch, Hugging Face (for NLP), OpenCV (for Computer Vision), Research papers on arXiv
Career Connection
Deep specialization makes you a highly sought-after candidate for niche AI roles, contributing to higher salary packages and roles in R&D or advanced tech companies in India.
Focus on Placement Readiness & Soft Skills- (Semester 7-8)
Actively participate in placement training, mock interviews, and group discussions organized by the college. Refine your communication, presentation, and teamwork skills. Prepare a strong resume highlighting projects and achievements.
Tools & Resources
KMIT Career Development Cell, LinkedIn Learning for soft skills, InterviewBit
Career Connection
Strong communication and interview skills are paramount for converting job offers, even with excellent technical knowledge, critical for securing placements in Indian corporate settings.
Network and Seek Mentorship- (Semester 6-8)
Connect with alumni, industry professionals, and faculty in the AI/ML domain through LinkedIn, college events, and conferences. Seek mentorship to gain insights into industry trends, career paths, and advanced learning resources.
Tools & Resources
LinkedIn, Professional AI/ML communities (e.g., DSC, Google Developers)
Career Connection
Networking can open doors to unexpected opportunities, referrals, and valuable career guidance, crucial for navigating the competitive Indian job market and future career growth.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together. Admission through TS-EAMCET counseling.
Duration: 4 years / 8 semesters
Credits: 160 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A10001 | Linear Algebra & Calculus | Core | 4 | Matrices and System of Linear Equations, Eigenvalues and Eigenvectors, Calculus of Single Variable, Partial Differentiation, Multiple Integrals |
| A10005 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry and Corrosion, Polymer Chemistry, Energy Sources, Spectroscopic Techniques |
| A10012 | Programming for Problem Solving | Core | 4 | Introduction to Programming, Control Structures, Functions and Arrays, Pointers and Strings, Structures and File Handling |
| A10501 | Engineering Graphics & Design | Core | 2 | Introduction to Engineering Graphics, Orthographic Projections, Isometric Projections, Projections of Solids, Introduction to CAD |
| A10051 | Engineering Chemistry Lab | Lab | 1.5 | Titrations, Conductometry, Potentiometry, Colorimetry, Instrumental Methods |
| A10052 | Programming for Problem Solving Lab | Lab | 1.5 | Basic C Programs, Conditional Statements, Loops and Arrays, Functions and Pointers, File Operations |
| A10054 | English Language & Communication Skills Lab | Lab | 1 | Phonetics, Vocabulary, Presentation Skills, Group Discussions, Role Plays |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A10002 | Differential Equations & Vector Calculus | Core | 4 | First Order Differential Equations, Higher Order Linear Differential Equations, Laplace Transforms, Vector Differentiation, Vector Integration |
| A10007 | Engineering Physics | Core | 3 | Wave Optics, Lasers and Fiber Optics, Quantum Mechanics, Semiconductor Physics, Magnetic Materials |
| A10011 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Three Phase Systems, Transformers, Electrical Machines |
| A10013 | Data Structures | Core | 3 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching |
| A10055 | Engineering Physics Lab | Lab | 1.5 | Lasers, Optics, Semiconductors, Magnetic Fields, Oscillations |
| A10056 | Basic Electrical Engineering Lab | Lab | 1.5 | Ohms Law, Circuit Theorems, AC Fundamentals, Transformers, Motors |
| A10058 | Data Structures Lab | Lab | 1.5 | Array Operations, Linked List Implementations, Stack and Queue Applications, Tree Traversals, Sorting Algorithms |
| A10351 | IT Workshop | Lab | 1 | PC Hardware, Operating Systems, Networking Basics, Productivity Tools, Web Technologies |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A10004 | Probability & Statistics | Core | 3 | Probability Distributions, Random Variables, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| A12401 | Discrete Mathematics | Core | 3 | Mathematical Logic, Set Theory, Relations and Functions, Graph Theory, Algebraic Structures |
| A12402 | Object Oriented Programming through Java | Core | 3 | OOP Concepts, Classes and Objects in Java, Inheritance and Polymorphism, Exception Handling, Multithreading and Collections |
| A12403 | Digital Logic Design | Core | 3 | Boolean Algebra, Combinational Circuits, Sequential Circuits, Registers and Counters, Memory and Programmable Logic |
| A12404 | Operating Systems | Core | 3 | OS Concepts, Process Management, CPU Scheduling, Memory Management, File Systems |
| A12451 | Object Oriented Programming through Java Lab | Lab | 1.5 | Java Basics, Inheritance Programs, Interface and Packages, Exception Handling, JDBC Connectivity |
| A12452 | Digital Logic Design Lab | Lab | 1.5 | Logic Gates, Combinational Circuits, Flip-Flops, Counters, Multiplexers |
| A10057 | Environmental Science | Mandatory Non-Credit | 0 | Ecosystems, Biodiversity, Pollution, Natural Resources, Sustainable Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A12405 | Database Management Systems | Core | 3 | DBMS Concepts, ER Modeling, Relational Algebra, SQL Queries, Transaction Management |
| A12406 | Computer Organization & Architecture | Core | 3 | Processor Organization, Instruction Set Architectures, Memory System, Input/Output Organization, Pipelining |
| A12407 | Design & Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| A12408 | Software Engineering | Core | 3 | Software Process Models, Requirements Engineering, Design Concepts, Software Testing, Project Management |
| A12409 | Foundations of Data Science | Core (AI&ML Specific) | 3 | Data Preprocessing, Exploratory Data Analysis, Data Visualization, Statistical Concepts for Data Science, Introduction to Machine Learning |
| A12453 | Database Management Systems Lab | Lab | 1.5 | SQL Commands, PL/SQL Programming, Triggers and Cursors, Database Normalization, ER Diagram Implementation |
| A12454 | Foundations of Data Science Lab | Lab | 1.5 | Python for Data Science, NumPy and Pandas, Data Cleaning, Matplotlib and Seaborn, Basic ML Model Implementation |
| A10059 | Constitution of India | Mandatory Non-Credit | 0 | Constitutional History, Fundamental Rights, Directive Principles, Union and State Government, Amendments |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A12410 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| A12411 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport and Application Layers |
| A12412 | Artificial Intelligence | Core (AI&ML Specific) | 3 | Introduction to AI, Search Algorithms, Knowledge Representation, Machine Learning Basics, Natural Language Processing |
| A12413 | Machine Learning | Core (AI&ML Specific) | 3 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Model Evaluation, Deep Learning Introduction |
| A124E1 | Professional Elective - I (e.g., Computer Graphics, Advanced Data Structures, etc.) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A12455 | Compiler Design Lab | Lab | 1.5 | Lexical Analyzer using LEX, Parser using YACC, Symbol Table Management, Intermediate Code Generation, Code Optimization Techniques |
| A12456 | Artificial Intelligence Lab | Lab | 1.5 | Prolog/Python for AI, Search Algorithm Implementation, Knowledge Representation Systems, Expert Systems, Mini-AI Projects |
| A12457 | Machine Learning Lab | Lab | 1.5 | Python Libraries for ML (Scikit-learn), Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Evaluation Metrics, Mini-ML Projects |
| A10060 | Gender Sensitization | Mandatory Non-Credit | 0 | Concepts of Gender, Gender Roles, Women''''s Studies, Feminist Movements, Gender Equality |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A12414 | Information Security | Core | 3 | Security Attacks, Cryptography, Network Security, Web Security, Security Management |
| A12415 | Big Data Analytics | Core (AI&ML Specific) | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce, Spark, Data Stream Processing |
| A12416 | Deep Learning | Core (AI&ML Specific) | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| A124E2 | Professional Elective - II (e.g., Computer Graphics, Advanced Data Structures, etc.) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A124O1 | Open Elective - I (From other departments) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A12458 | Big Data Analytics Lab | Lab | 1.5 | Hadoop HDFS Operations, MapReduce Programming, Spark RDD and DataFrames, Hive and Pig Queries, Big Data Mini Project |
| A12459 | Deep Learning Lab | Lab | 1.5 | TensorFlow/Keras/PyTorch Basics, Implementing CNNs for Image Classification, Implementing RNNs for Sequence Data, Transfer Learning, Deep Learning Mini Project |
| A12460 | Mini Project with Python | Project | 2 | Problem Identification, Requirements Gathering, System Design, Coding and Testing, Report Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A124E3 | Professional Elective - III (e.g., Reinforcement Learning, Computer Vision, etc.) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A124E4 | Professional Elective - IV (e.g., Natural Language Processing, Robotics, etc.) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A124O2 | Open Elective - II (From other departments) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A12461 | Industry Oriented Mini Project/Internship | Project/Internship | 2 | Industry Problem Solving, Practical Skill Application, Teamwork, Professional Communication, Reporting and Presentation |
| A12462 | Project Phase - I | Project | 6 | Problem Statement Definition, Literature Survey, System Design, Technology Selection, Initial Implementation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| A124E5 | Professional Elective - V (e.g., IoT for AI, Ethical AI, etc.) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A124O3 | Open Elective - III (From other departments) | Elective | 3 | Key topics vary by elective choice, Consult specific elective syllabus |
| A12463 | Project Phase - II | Project | 10 | Advanced Implementation, Testing and Validation, Performance Evaluation, Documentation, Final Presentation and Viva |
| A12464 | Technical Seminar | Seminar | 2 | Research Skill Development, Topic Selection, Literature Review, Presentation Skills, Report Writing |
