Semester 4 Syllabus & Notes

This course covers the basics of organization and architecture of a digital computer, including concepts of instruction sets, microprocessor design, input-output units, and memory systems. It also analyzes modern computing architectures, Verilog HDL, and trends in computer architecture.

Course study material, comprehensive notes, subjective model answers, and practice MCQs for CSE273 - CSE273.

This course focuses on Java programming constructs, Object-Oriented Programming principles, inheritance, exception handling, multithreading, and the use of collections and generics. It aims to develop skills in creating efficient, reusable code and managing advanced Java applications involving database connectivity.

This course covers the fundamentals of operating systems, including OS structure, process management, CPU scheduling, synchronization, and deadlock handling. Students will also learn about memory management techniques like paging and segmentation, as well as disk scheduling methods.

This laboratory course covers practical operating systems concepts including Linux command-line tools, Bash shell scripting, low-level file and directory operations using OS system calls, process and thread management, and inter-process communication mechanisms.

This course covers the basic techniques of analyzing algorithms using space and time complexity, asymptotic notations, and various algorithm design techniques such as brute force, divide and conquer, dynamic programming, and greedy algorithms. Students will also learn string matching algorithms, backtracking, branch and bound techniques, and basic concepts of complexity classes.

This course covers the essentials of Artificial Intelligence, from foundational concepts and search algorithms to machine learning, deep learning, and generative AI. Students will learn to apply various AI techniques and tools to solve real-world problems, including model deployment and MLOps.

This subject provides an overview of random variables, probability distributions, correlation, regression, and statistical tests, enabling students to apply these concepts to engineering problems and analyze estimators.

This course aims to develop students' analytical skills by applying procedural fluency with number systems and mathematical operations to solve problems related to percentage, profit & loss, data series, ratio, proportion, permutation, combination, and reasoning.

The course focuses on developing advanced analytical skills, including numeration, percentage, ratio, counting methods, and logical reasoning, to prepare students for competitive examinations and enhance their employability.