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
Randomized Algorithms for Robustness
Was this section helpful?
- Random Forests, Leo Breiman, 2001 Machine Learning, Vol. 45 DOI: 10.1023/A:1010933404324 - A foundational paper introducing the Random Forest algorithm, detailing how bootstrapping and random feature selection enhance predictive accuracy and prevent overfitting.
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, 2014 Journal of Machine Learning Research, Vol. 15 (JMLR) - The seminal paper introducing dropout as a regularization technique for neural networks, explaining its mechanism and effectiveness in reducing overfitting.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) - A comprehensive textbook covering statistical learning methods, including ensemble methods (Bagging, Random Forests) and regularization, providing theoretical foundations. 2nd edition.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - An authoritative textbook on deep learning, featuring detailed explanations of dropout, stochastic gradient descent, and other neural network optimization and regularization techniques.