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
Connecting Strategies to ML Model Implementation
Was this section helpful?
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - Comprehensive coverage of statistical learning methods, including decision trees, ensemble methods, support vector machines, and optimization algorithms, illustrating various algorithmic strategies.
- Reinforcement Learning: An Introduction, Richard S. Sutton, Andrew G. Barto, 2018 (A Bradford Book, The MIT Press) - Definitive textbook on reinforcement learning, detailing dynamic programming applications for solving Markov Decision Processes in value and policy iteration.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a thorough theoretical and practical foundation for deep learning, covering optimization techniques (e.g., SGD) and regularization methods (e.g., Dropout) that exemplify iterative and randomized algorithms.
- Random Forests, Leo Breiman, 2001 Machine Learning, Vol. 45 DOI: 10.1023/A:1010933404324 - The seminal paper introducing the Random Forest algorithm, demonstrating a powerful combination of divide and conquer, greedy, and randomized strategies for ensemble learning.