Data Structures and Algorithms, Level II: Hashing, Trees, Graphs (ML Foundations Series)

• Hashing for efficiency: Builds on introductory DSA (Data Structures and Algorithms) concepts to cover hashing, a critical technique for instantaneous data look-up regardless of dataset size.
• Tree-based ML models: Introduces tree-based data structures, which enable powerful machine learning models such as random forest and XGBoost.
• Graph-based ML computation: Covers graph-based data structures, which are key to lower-level representations in popular ML libraries like TensorFlow and PyTorch, including for highly flexible neural-network models.

The Machine Learning Foundations series of online trainings provides a comprehensive overview of all of the subjects — mathematics, statistics, and computer science — that underlie contemporary machine learning techniques, including deep learning and other artificial intelligence approaches. Extensive curriculum detail can be found at the course’s GitHub repo.

All of the classes in the ML Foundations series bring theory to life through the combination of vivid full-color illustrations, straightforward Python examples within hands-on Jupyter notebook demos, and comprehension exercises with fully-worked solutions.

The focus is on providing you with a practical, functional understanding of the content covered. Context will be given for each topic, highlighting its relevance to machine learning. You will be better positioned to understand cutting-edge machine learning papers and you will be provided with resources for digging even deeper into topics that pique your curiosity.

There are 14 classes in the series, organized into four subject areas:

1. Linear Algebra (three classes)

• Linear Algebra for Machine Learning: Intro
• Linear Algebra for Machine Learning, Level II: Matrix Tensors
• Linear Algebra for Machine Learning, Level III: Eigenvectors

2. Calculus (four classes)

• Calculus for Machine Learning: Intro
• Calculus for Machine Learning, Level II: Automatic Differentiation
• Calculus for Machine Learning, Level III: Partial Derivatives
• Calculus for Machine Learning, Level IV: Gradients & Integrals

3. Probability and Statistics (four classes)

• Intro to Probability Theory
• Probability II and Information Theory
• Intro to Statistics
• Statistics II: Regression and Bayesian

4. Computer Science (three classes)

• Intro to Data Structures and Algorithms
• DSA II: Hashing, Trees, and Graphs
• Optimization

Each of the four subject areas are fairly independent, however theory within a given subject area generally builds over the 3-4 classes — topics in later classes of a given subject area often assume an understanding of topics from earlier classes. Work through the individual classes based on your particular interests or your existing familiarity with the material.

(Note that at any given time, only a subset of the ML Foundations classes will be scheduled and open for registration.)

This class, DSA II: Hashing, Trees, and Graphs, builds on the Intro to DSA class to demonstrate hashing, a critical computer science technique that facilitates instantaneous data look-up no matter how large a set of data points you’re working with. Tree-based data structures are introduced, enabling powerful machine learning models such as random forest and XGBoost. Finally, graph-based data structures are covered. Graphs are essential because they are how many popular ML libraries (e.g., TensorFlow, PyTorch) represent computations, including infinitely flexible neural-network (e.g., deep learning) models.