Semester 5
B.Tech (Artificial Intelligence & Machine Learning)
Subjects
Operating Systems
This course is intended to describe the fundamentals of operating systems, programming threads and concurrency, deadlock handling and memory management techniques, file systems and input/output operations, giving students an exposure to the file management systems. The course will also cover the functionality of the operating system, multiprocessor scheduling, process synchronization and several case studies of operating systems.
Design and Analysis of Algorithms
This course is designed to enable the student to design and analyze algorithms for the problems. This course covers basic strategies of algorithm design: top-down design, divide and conquer, asymptotic costs, applications to sorting and searching, matrix algorithms, shortest-path and spanning tree problems, dynamic programming, greedy algorithms and graph algorithms.
Fundamentals of Deep Learning
The main objective of this course is to develop the understanding of key mathematical principles which are used behind the working of neural networks. Convolution Neural Networks and Recurrent Neural Networks have also been covered in this course. This course also provides the details for usage of Deep Learning for Natural Language Processing.
Computer Organization & Architecture
This course enables the students to understand the principles of computer organization and the basic architectural concepts. It begins with basic organization, design, and programming of a simple digital computer and introduces simple register transfer language to specify various computer operations. Topics include computer arithmetic, instruction set design, microprogrammed control unit, pipelining and vector processing, memory organization and I/O systems, and multiprocessors.
Introduction to Internet of Things
The course enables student to understand the basics of Internet of things and protocols. It introduces some of the application areas where Internet of Things can be applied. Students will learn about the middleware for Internet of Things. The course addresses various components of Internet of things such as Sensors, internetworking, protocols. In the end students will also be able to design and implement IoT circuits and solutions.
Principles of Entrepreneurship Mindset
This course gives exposure to the students for the core entrepreneurship concepts. Three real time case studies have been covered to give the students real time understanding of setting up a startup. Business canvas model has been covered under the syllabus followed by the finance and marketing skills for budding entrepreneurs. Students will be able to create and write a business plan after the completion of the course.
Operating Systems Lab
This course includes the use of operating systems and concepts and threads as well as the shell programming concepts. Students will execute the various system calls, CPU scheduling algorithms, memory management, page replacement algorithms and file system-using commands.
Design and Analysis of Algorithms Lab
This course is designed to enable the student to design algorithmic approaches of solutions to problems based on dynamic programming and greedy approaches. It also discusses analysis of algorithms and time and space complexity of the algorithms.
Fundamentals of Deep Learning Lab
This course includes understanding Convolution Neural Network for object classification from images. Understand and implement various deep learning approaches in Python.
Introduction to Internet of Things Lab
This course is designed to teach students to analyse different controller boards, simulation platforms and applications of IoT. The course includes real based mini projects using Arduino.