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
Why Data Structures Matter for ML Performance
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- Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 2022 (MIT Press) - This standard textbook provides comprehensive coverage of data structures and algorithms, including complexity analysis (O(n), O(1)), hash tables, lists, and trees, all of which are essential background for understanding ML performance.
- Designing Machine Learning Systems, Chip Huyen, 2022 (O'Reilly Media) - This book offers a practical perspective on building machine learning systems, emphasizing how efficient data handling and algorithmic choices impact system performance, scalability, and resource requirements in real-world ML applications.
- Feature Hashing for Large-scale Multitask Learning, Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, Josh Attenberg, 2009 Proceedings of the 26th International Conference on Machine Learning (ICML) (International Machine Learning Society (IMLS)) DOI: 10.48550/arXiv.0902.2206 - This paper introduces feature hashing, a method for transforming high-dimensional data into a lower-dimensional representation using hash functions, which is directly referenced in the section for efficient feature engineering.
- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - Authored by the creator of Pandas, this book explains how libraries like Pandas leverage efficient underlying data structures (built on NumPy arrays) for fast data loading, preparation, and manipulation, as highlighted in the section.
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