Data Structures and Algorithms for Machine Learning
Chapter 1: Foundations: DSA in the Machine Learning Context
Why Data Structures Matter for ML Performance
Complexity Analysis for ML Practitioners
Python's Built-in Structures in ML Workflows
NumPy Arrays: The Foundation of Numerical ML
Pandas DataFrames for Data Preparation
Mapping ML Problems to Data Structures
Practice: Profiling Basic Data Operations
Quiz for Chapter 1
Chapter 2: Trees for Searching, Indexing, and Modeling
Fundamentals of Tree Structures
Binary Search Trees for Efficient Lookup
Importance of Balanced Trees
Decision Trees: Structure and Algorithms
Tree Ensembles: Random Forests and Gradient Boosting
Tree Traversal Techniques
Hands-on Practical: Implementing Tree Operations
Quiz for Chapter 2
Chapter 3: Hashing for Feature Engineering and Similarity Search
Hash Functions and Hash Tables
Handling Hash Collisions
Feature Hashing for Dimensionality Reduction
Introduction to Locality-Sensitive Hashing (LSH)
Implementing Hash-Based Structures in Python
Performance Trade-offs with Hashing
Practice: Implementing Hashing Techniques
Quiz for Chapter 3
Chapter 4: Graphs for Relational Data and Network Models
Representing Graphs: Adjacency Lists and Matrices
Graph Traversal: Breadth-First Search (BFS)
Graph Traversal: Depth-First Search (DFS)
Shortest Path Algorithms Overview
Graph Embeddings for Node Representation
Applications in Recommendation Systems and NLP
Hands-on Practical: Graph Representation and Traversal
Quiz for Chapter 4
Chapter 5: Priority Queues and Heaps for Optimization Tasks
Heap Data Structure Properties
Core Heap Operations
Implementing Priority Queues with Heaps
Applications in Selection Problems
Role in Supporting Complex Algorithms
Python's heapq Module
Practice: Using Heaps for Selection
Quiz for Chapter 5
Chapter 6: Core Algorithmic Strategies in Machine Learning
Divide and Conquer Approach
Dynamic Programming Principles
Greedy Algorithms in Optimization
Randomized Algorithms for Robustness
Iterative Optimization Algorithms
Connecting Strategies to ML Model Implementation
Practice: Identifying Strategies in ML Libraries
Quiz for Chapter 6
Graph Embeddings for Node Representation
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- DeepWalk: Online Learning of Social Representations, Bryan Perozzi, Rami Al-Rfou, Steven Skiena, 2014 KDD '14: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2623330.2623732 - This foundational paper introduces DeepWalk, a pioneering random walk-based method for learning node embeddings by treating random walks as "sentences" and applying Word2Vec-like techniques.
- node2vec: Scalable Feature Learning for Networks, Aditya Grover, Jure Leskovec, 2016 KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939754 - This paper extends DeepWalk by proposing node2vec, an algorithm that learns node embeddings through biased random walks, allowing it to capture a flexible balance between BFS-like and DFS-like neighborhood exploration.
- Graph Neural Networks: A Review of Methods and Applications, Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, and Liqiang Nie, 2021 AI Open, Vol. 1 DOI: 10.1016/j.aiopen.2021.01.001 - A comprehensive survey providing an overview of various Graph Neural Network (GNN) models, their applications, and future research directions, relevant for understanding this powerful family of graph embedding techniques.