Lesson 1: Orientation to Data Structures and Algorithms
In Lesson 1, Jon provides an orientation to data structures and algorithms. He starts by familiarizing you with his Machine Learning Foundations curriculum and then provides you with historical context on both data and algorithms. He concludes with a discussion of applications of data structures and algorithms to the field of machine learning.
Lesson 2: “Big O” Notation
Lesson 2 focuses on “big O” notation, a fundamental computer science concept that is a prerequisite for understanding almost everything else in these LiveLessons. Jon explores three of the most common “big O” runtimes: constant, linear, and polynomial. He wraps up the lesson with an overview of the other common runtimes and performance variation based on the particular data you are working with.
Lesson 3: List-Based Data Structures
Lesson 3 is all about list-based data structures. Jon surveys all of the key types, including arrays, linked lists, stacks, queues, and deques.
Lesson 4: Searching and Sorting
In Lesson 4, Jon helps you hone your understanding of “big O” notation by applying searching and sorting algorithms to lists. Specifically, he covers binary search and three exemplary sorting algorithms: bubble, merge, and quick.
Lesson 5: Sets and Hashing
In Lesson 5, Jon details maps and dictionaries, which are types of sets. He digs into hash functions, which enable mind-bogglingly efficient data retrieval, including taking into account collisions, load factor, hash maps, string keys, and machine learning applications.
Lesson 6: Trees
In Lesson 6, Jon provides you with an introduction to the trees, a hugely useful data structure in machine learning. He presents specific hands-on examples involving decision trees, random forests, and gradient boosting.
Lesson 7: Graphs
Lesson 7 provides you with an introduction to graphs, another hugely useful data structure in machine learning. Jon discusses graph direction and cycles before wrapping up the coverage of data structures and algorithms with a note on DataFrames and his recommended resources for further study of the computer science field.
Lesson 8: Machine Learning Optimization
With Lesson 8, Jon shifts gears from data structures and algorithms to machine learning-specific optimization. He starts off by discussing when statistical optimization approaches break down and then digs into objective functions, particularly mean absolute error and mean squared error. Jon carries on by detailing how to optimize objective functions with gradient descent and what critical points are. He concludes the lesson with neat tricks like mini-batch sampling, learning rate scheduling, and gradient ascent.
Lesson 9: Fancy Deep Learning Optimizers
Lesson 9 wraps a bow not only on these particular LiveLessons but also on Jon’s entire Machine Learning Foundations series. In this lesson Jon provides an overview of Jacobian and Hessian matrices as well as the fancy deep learning optimizers they facilitate that have momentum and are adaptive. Jon leaves you with his recommended next steps for moving forward with your machine learning journey.
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Introduction
Data Structures, Algorithms, and Machine Learning Optimization: Introduction
Lesson 1: Orientation to Data Structures and Algorithms
Topics
1.1 Orientation to the Machine Learning Foundations Series
1.2 A Brief History of Data
1.3 A Brief History of Algorithms
1.4 Applications to Machine Learning
Lesson 2: “Big O” Notation
Topics
2.1 Introduction
2.2 Constant Time
2.3 Linear Time
2.4 Polynomial Time
2.5 Common Runtimes
2.6 Best versus Worst Case
Lesson 3: List-Based Data Structures
Topics
3.1 Lists
3.2 Arrays
3.3 Linked Lists
3.4 Doubly-Linked Lists
3.5 Stacks
3.6 Queues
3.7 Deques
Lesson 4: Searching and Sorting
Topics
4.1 Binary Search
4.2 Bubble Sort
4.3 Merge Sort
4.4 Quick Sort
Lesson 5: Sets and Hashing
Topics
5.1 Maps and Dictionaries
5.2 Sets
5.3 Hash Functions
5.4 Collisions
5.5 Load Factor
5.6 Hash Maps
5.7 String Keys
5.8 Hashing in ML
Lesson 6: Trees
Topics
6.1 Introduction
6.2 Decision Trees
6.3 Random Forests
6.4 XGBoost: Gradient-Boosted Trees
6.5 Additional Concepts
Lesson 7: Graphs
Topics
7.1 Introduction
7.2 Directed versus Undirected Graphs
7.3 DAGs: Directed Acyclic Graphs
7.4 Additional Concepts
7.5 Bonus: Pandas DataFrames
7.6 Resources for Further Study of DSA
Lesson 8: Machine Learning Optimization
Topics
8.1 Statistics versus Machine Learning
8.2 Objective Functions
8.3 Mean Absolute Error
8.4 Mean Squared Error
8.5 Minimizing Cost with Gradient Descent
8.6 Gradient Descent from Scratch with PyTorch
8.7 Critical Points
8.8 Stochastic Gradient Descent
8.9 Learning Rate Scheduling
8.10 Maximizing Reward with Gradient Ascent
Lesson 9: Fancy Deep Learning Optimizers
Topics
9.1 Jacobian Matrices
9.2 Second-Order Optimization and Hessians
9.3 Momentum
9.4 Adaptive Optimizers
9.5 Congratulations and Next Steps
Summary
Data Structures, Algorithms, and Machine Learning Optimization: Summary