Search results: 266
The course on Algorithms introduces students to the fundamental principles of problem-solving and efficient computation. It covers the design and analysis of algorithms, including asymptotic complexity, sorting and searching techniques, graph algorithms, and advanced paradigms such as divide and conquer, greedy methods, dynamic programming, and backtracking. The course also explores hashing, string matching, and concepts of NP-completeness, emphasizing both theoretical understanding and practical applications. By the end of the course, students gain the ability to analyze problems, design optimized solutions, and apply algorithms effectively in real-world scenarios.
- Teacher: DR PRASHANT AGARWAL [MCA]
- Teacher: Shish Pal Jatav [MCA]
Objective - This course introduces the foundational concepts
of Machine Learning and its integration into modern Generative AI systems. It
emphasizes the application of supervised, unsupervised, and deep learning
models – including transformers and LLMs – along with practical exposure to
prompt engineering, generative models, and ethical AI practices.
- Teacher: MS NEELAM RAWAT [MCA]
| CO1 | Demonstrate frontend web application using React JS. | ||||
| CO2 | Illustrate navigation and hooks in front end web application using React Router and React Hooks Library | ||||
| CO3 | Apply React AXIOS Library to fetch RESTful API. | ||||
| CO4 | Analyse backend web apps using Node JS. | ||||
| CO5 | Test RESTful API using Node JS. | ||||
- Teacher: Mr. Saurabh Choudhary [MCA]
| CO1 | Demonstrate frontend web application using React JS. | ||||
| CO2 | Illustrate navigation and hooks in front end web application using React Router and React Hooks Library | ||||
| CO3 | Apply React AXIOS Library to fetch RESTful API. | ||||
| CO4 | Analyse backend web apps using Node JS. | ||||
| CO5 | Test RESTful API using Node JS. | ||||
Operating System
CS206L - CSIT - 3-C
Faculty: Dr. Harsh Khatter
Credit: 3-0-0
- Teacher: Dr. HARSH KHATTER [CO]
- Teacher: MR BHOOPENDRA KUMAR [CSI]
- Teacher: Yousuf Haider [CSIT]
IE
KME-503
- Teacher: Mr. Anandkumar Prajapati [CSE]
- Teacher: Ms. Maitree [CSE]
Syllabus outline for the MBA course Data Mining Techniques – Predictive Modeling & Pattern Discovery using R (MS617E) offered at KIET Group of Institutions, Delhi-NCR.
It covers data mining technologies, data warehousing & preprocessing, predictive modeling, R programming, and data analytics in R over 30 lecture hours. The course aims to build skills in applying data mining techniques, predictive models, and R programming for business problem-solving.
Key outcomes include applying data warehousing, preprocessing, predictive modeling, and evaluating data analytics in real-world contexts. Evaluation is based on MSE, CA (class assessments), and ESE, totaling 150 marks.
Evaluation is through MSE (50 marks), CA (25 marks), and ESE (75 marks), totaling 150 marks.
- Teacher: Ashima Arya [CSIT]
- Teacher: Latika Sharma [CSIT]
Attempt your C program here with output.