Instructor Profile:
Dr. Sarika Ashok Kondekar
Ph.D. in Computer Science | Researcher | Educator
Dr. Sarika Ashok Kondekar is a seasoned academic and researcher with a Ph.D. in Computer Science from Dr. Babasaheb Ambedkar Marathwada University, specializing in Digital Image Processing. Her doctoral research focused on the development of an “Automated Attendance System Using Face Recognition,” showcasing her expertise in artificial intelligence and computer vision.
With over 16 years of experience in higher education, Dr.Kondekar is dedicated to fostering a dynamic learning environment. She has published more than 12 research papers in reputed international journals and conferences, including SCI/Scopus-indexed publications. A regular presenter at academic conferences, she is committed to advancing the field of computer science through her research and collaboration.
In her current role as ERP Coordinator at K.K. Wagh Arts, Commerce, and Science College, Dr.Kondekar plays a key role in integrating technology into educational systems, driving administrative efficiency, and enhancing student engagement. Her passion for inspiring future computer scientists is evident in her innovative teaching methods, combining theoretical foundations with practical applications to equip students for the evolving tech landscape.
Dr.Kondekar’s expertise and commitment make her an ideal educator for MOOCs, where she continues to share her knowledge with a global audience, nurturing the next generation of tech innovators.
Course Outline: Discrete Mathematics – Huffman Coding
Module 1: Introduction to Coding Theory and Discrete Mathematics Concepts
- Fundamentals of Discrete Mathematics: Sets, Relations, Functions, and Graph Theory
- Introduction to Coding Theory: Overview, applications, and relevance in data compression
- Binary Trees and Prefix Codes: Understanding the structure and significance of prefix codes in coding
- Problem Solving: Exercises and examples to explore coding theory principles using discrete structures
Module 2: Huffman Coding Algorithm
- Overview of Huffman Coding: History, use cases, and importance in lossless data compression
- Constructing Huffman Trees: Step-by-step process of building the Huffman Tree from frequency tables
- Encoding and Decoding: Techniques for generating binary codes and reconstructing original data
- Practical Application: Hands-on exercises in constructing Huffman codes for given data sets
Module 3: Efficiency of Huffman Coding
- Optimality of Huffman Coding: Understanding why Huffman Coding is optimal for lossless compression
- Compression Ratio and Entropy: Calculation and evaluation of efficiency
- Comparative Analysis: Comparing Huffman Coding with other compression algorithms (Shannon-Fano, LZW)
- Real-World Case Studies: Exploring Huffman Coding’s role in file compression standards (JPEG, PNG, etc.)
Module 4: Advanced Concepts and Applications
- Adaptive Huffman Coding: Introduction to dynamic Huffman algorithms for real-time applications
- Applications in Data Compression: Use in multimedia, text files, and communication systems
- Challenges and Limitations: Understanding limitations and scenarios where Huffman Coding may not be ideal
- Project and Assessment: Students will apply their knowledge by implementing Huffman Coding in a real-world scenario and present their findings
This course provides a comprehensive understanding of Huffman Coding within the framework of Discrete Mathematics, empowering students to apply these concepts in various computational fields.
