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
Performance Trade-offs with Hashing
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- Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2009 (The MIT Press) - Standard reference for hash table theory, including average-case and worst-case performance analysis, hash function design, and various collision handling methods.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, 2014 (Cambridge University Press) DOI: https://doi.org/10.1017/CBO9781139924801 - Provides a clear and comprehensive explanation of Locality-Sensitive Hashing (LSH) for approximate nearest neighbor search, detailing its methodology, applications, and the trade-offs between speed and accuracy.
- Approximate nearest neighbors: Towards removing the curse of dimensionality, Piotr Indyk, Rajeev Motwani, 1998 Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing (ACM) DOI: 10.1145/276698.276876 - The seminal paper that introduced Locality-Sensitive Hashing (LSH), laying the theoretical groundwork for efficient approximate similarity search in high-dimensional data.