Course11110500160 lectures
Design and Analysis of Algorithms with Applications
Prof. Srinivasa Rao Pentyala · IIT-ISM Dhanbad
About
(i). The course is a fundamental component of Mathematics and computer science and other engineering disciplines. It provides a systematic framework for understanding how to design efficient computational procedures and how to evaluate their performance. Algorithms form the backbone of every software system, governing data processing etc.(ii). Sequence of well-defined instructions for solving a problem or performing a computation. Students learn to distinguish between an algorithm and a mere program, emphasizing correctness, efficiency, and scalability. (iii). The design aspect of the course focuses on various paradigms and strategies used to construct algorithms for different classes of problems. These include divide and conquer, greedy methods, dynamic programming, backtracking, branch and bound, and graph-based approaches. Each paradigm is explored through classical examples like sorting, searching, shortest paths, and optimization problems.(iv). This course focus on application of algorithms in various real life systems such as design of machine precision units of mechanical engineering, Image processing for medical applications, risk management in reliability systems, scientific applications such as weather prediction, smart farming etc.
Intended audience: Under graduate and post graduate students of Mathematics, Computer Science, Information Technology etc.
Prerequisites: Basic knowledge of programming of any computer language is sufficient and no specific prerequisite it demands as such.
Lectures
Week 1
- Lecture 01: Basic Concepts of Algorithms
- Lecture 02: Performance and Asymptotic Analysis
- Lecture 03: Mathematical Notations and Recursive Algorithms
- Lecture 04: Mathematical Notations and case Studies
- Lecture 05: Greedy Techniques
Week 2
- Lecture 06: The 0 –1 KanapsackProblems
- Lecture 07: Huffman Algorithm & Knapsack Problem
- Lecture 08: Review of Graph Algorithms
- Lecture 09: Breadth First Search Algorithm (BFS)
- Lecture 10: Depth First Search Algorithm (DFS)
Week 3
- Lecture 11: Kruskal’s Algorithms
- Lecture 12: Prims’s Algorithms
- Lecture 13: Stable Matching Problem
- Lecture 14: Shortest Path Problem
- Lecture 15: Union Find data Structure
Week 4
- Lecture 16: Divide and Conquer Algorithms
- Lecture 17: Heap Sort, Bucket Sort Algorithms
- Lecture 18: Binary Search Tree Algorithms
- Lecture 19: Optimal Storage on Taps Algorithms
- Lecture 20: Stassen’s matrix multiplication Algorithms
Week 5
- Lecture 21: Bellman-Ford algorithm
- Lecture 22: Examples: Bellman-Ford algorithm
- Lecture 23: Examples: Bellman-Ford algorithm
- Lecture 24: All pairs shortest path problem
Week 6
- Lecture 25: Examples
- Lecture 26: Representation of Polynomials and Complexity
- Lecture 27: The DFT Algorithms and Complexity
- Lecture 28: Representation of polynomials
- Lecture 29: The DFT and FFT
Week 7
- Lecture 30: Efficient FFT and implementation
- Lecture 31: Network Algorithms & Complexity
- Lecture 32: Approximation Algorithms
- Lecture 33: Approximation Algorithms
- Lecture 34: Parallel Algorithms
Week 8
- Lecture 35: Online Algorithms
- Lecture 36: Algorithmic Trading, Portfolio Optimization
- Lecture 37: Risk Analysis and Fraud Detection
- Lecture 38: Risk Analysis and Fraud Detection: Approximation Algorithms
Week 9
- Lecture 39: Logistics and Supply Chain Management
- Lecture 40: Scheduling, Inventory Management
- Lecture 41: Bioinformatics: Applications of Algorithms
- Lecture 42: Applications involve sequence alignment,
- Lecture 43: Gene sequencing, protein prediction
- Lecture 44: Complex Biological Data Analysis Tasks
Week 10
- Lecture 45: Applications in Database Systems of Large Computation
- Lecture 46: Applications in Database Systems
- Lecture 47: Fundamental Algorithms for Sorting, Searching
- Lecture 48: Fundamental Algorithms for Sorting and Pattern Matching
Week 11
- Lecture 49: Searching and Data Manipulation
- Lecture 50: Web Applicatons
- Lecture 51: Applications in Data Science and Machine Learning
- Lecture 52: Data Analysis Applications, Pattern Recognition
- Lecture 53: Optimization in Machine Learning
- Lecture 54: Clustering, Classification
Week 12
- Lecture 55: Regression, and Neural Networks,WeatherPredictions
- Lecture 56: Big Data Processing: Developing
- Lecture 57: Data Mining, Natural Language Processing
- Lecture 58: Advanced Machine Learning for handling
- Lecture 59: Extracting Insights from massive Datasets
- Lecture 60: Extracting insights from Massive Datasets (Conti..)
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT-ISM Dhanbad
- Department
- Mathematics
- Teacher
- Prof. Srinivasa Rao Pentyala
- Level
- Undergraduate
- Language
- English
- Subjects
- Mathematics
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Kharagpur.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed