Tag: geca

All the articles with the tag "geca".

• Mapping of GECA's academic resources

   Updated: May 8, 2025  5 hours read

  This post mean't to index all the links, resources, labs, and notes made for GECA's academics. Here are all the links, categorized subject wise.

• CSPCC5004. Social Network Analysis in Data Mining

   Published: Apr 24, 2025  20 min read

  A focused presentation on graph clustering methods, similarity measures, and real-world use cases of social network analysis in advanced data mining.

• CS3016/04. Implement approximate algorithms on various variants of Travelling Salesman Problem (TSP).

   Updated: Apr 21, 2025  3.5 hours read

  Understand and implement approximation algorithms for classical and metric variants of the Travelling Salesman Problem (TSP), including MST-based and Christofides' heuristic.

• CS3016/05. Demonstrate randomness by implementing Quicksort algorithm.

   Published: Apr 21, 2025  2 hours read

  Understand the concept of algorithmic randomness through randomized quicksort implementation. Analyze performance, visualize sorting, and compare with deterministic behavior.

• CS3016/06. Demonstrate randomness by implementing Min-Cut algorithm.

   Published: Apr 21, 2025  2.5 hours read

  Implement and explore the randomized Min-Cut algorithm to understand the role of randomness in algorithms and graph partitioning.

• CS3016/07. Demonstrate with a Program the Markov Property and Stationary Markov Chains

   Published: Apr 21, 2025  2.5 hours read

  Understand and simulate the Markov property and stationary distributions in Markov chains using Python. Gain insights into stochastic systems with memoryless transitions.

• CS3016/08. Implementing the Viterbi algorithm for Hidden Markov Models (HMMs)

   Published: Apr 21, 2025  3 hours read

  Understand and implement the Viterbi algorithm, a dynamic programming method for decoding the most likely state sequence in Hidden Markov Models.

• CS3016/09. Implementing the Forward Algorithm

   Published: Apr 21, 2025  2.5 hours read

  Understand and implement the Forward Algorithm, a dynamic programming technique used to compute sequence probabilities in Hidden Markov Models (HMMs).

• CS3016/10. Implementing algorithms from geometry problems and large data sets

   Published: Apr 21, 2025  3.5 hours read

  Explore and implement classic algorithms from computational geometry and data-intensive problems. Emphasis on real-world applications and scalable solutions.

• CS3016/03. Implement approximate algorithm techniques for Vertex Cover and Steiner Tree problems.

   Updated: Apr 2, 2025  3 hours read

  Learn and implement approximation algorithms for NP-hard problems like Vertex Cover and Steiner Tree. Understand their practical importance, strategies, and performance guarantees.