My Favorite Calculus Resources — Topic 92 of Machine Learning Foundations

Jon Krohn
16 Mar 202204:35
EducationalLearning
32 Likes 10 Comments

TLDRIn this final video of the Machine Learning Foundation series, the presenter shares their favorite external calculus resources and concludes the integral calculus segment. They recommend Dyson Rothedale's book for differential calculus and Zhang Doll's 'Dive into Deep Learning' for integral calculus, along with visual-focused content from 'Three Blue One Brown' on YouTube. The video recaps the series, which covered binary classification models, the confusion matrix, and the ROC curve, and delved into manual and numeric integration to understand model parameter fitting. With calculus under their belt, viewers are now prepared to tackle the remaining subjects, starting with probability theory and information theory, which are crucial for understanding ML models' probabilistic nature. The presenter encourages viewers to subscribe for the next video on these topics and to engage with their content through likes, comments, and newsletter subscriptions.

Takeaways
  • 🎓 **Final Video in Series**: This is the last video on calculus in the machine learning foundation series.
  • 📚 **Differential Calculus Resource**: For further study on differential calculus, Dyson Rothedale's book and Three Blue One Brown's YouTube channel are recommended.
  • 📈 **Integral Calculus Resources**: The same resources as for differential calculus are suggested for integral calculus, plus Appendix 18.5 of Zhang Doll's 'Dive into Deep Learning'.
  • 🔢 **Binary Classification Models**: The video discussed binary classification models and the confusion matrix, which is used to create an ROC curve.
  • 📉 **Understanding ROC Curve**: The area under the ROC curve was explored, which is a measure of the model's performance.
  • 🧮 **Manual vs. Numerical Integration**: The video covered how to calculate integrals manually and then how to use numerical integration in Python to automate the process.
  • 🔍 **Partial Derivatives and Gradients**: Partial derivatives were explained and their use in determining gradients for machine learning cost functions was detailed.
  • 🎯 **Gradient Descent**: The importance of partial derivative calculus for understanding gradient descent in machine learning optimization was emphasized.
  • 📊 **Probabilistic Models**: Upcoming subjects in the series will cover probability theory and information theory, which are fundamental to all probabilistic machine learning models.
  • 🧠 **Understanding Uncertainty**: Probability theory helps in understanding and making decisions in the face of uncertainty.
  • 📺 **Stay Updated**: Encouragement to subscribe to the channel for the next video in the series.
  • 💌 **Connect with the Creator**: Options provided to follow up with the presenter through his website, email newsletter, LinkedIn, and Twitter.
Q & A

  • What is the main focus of the final calculus video in the machine learning foundation series?

    -The main focus of the final calculus video is to provide favorite external calculus resources and to summarize the entire subject area covered in the series.

  • Which chapter of Dyson Rothedale's book is recommended for learning more about differential calculus?

    -Chapter six of Dyson Rothedale's 'Mathematics for Machine Learning' book is recommended for further study on differential calculus.

  • What YouTube channel offers visual-focused videos on differential calculus topics?

    -The YouTube channel 'three blue one brown' offers a variety of visual-focused videos on differential calculus topics.

  • What additional resource is recommended for integral calculus besides the ones for differential calculus?

    -Appendix 18.5 of Zhang Doll's 'Dive into Deep Learning' book is recommended as an additional resource for integral calculus.

  • What is the confusion matrix and how is it used in the context of the video?

    -The confusion matrix is a table used to describe the performance of a binary classification model. In the video, it is used to create a Receiver Operating Characteristic (ROC) curve.

  • How is integral calculus used in the process of finding the area under the ROC curve?

    -Integral calculus is used to calculate the area under the ROC curve manually and also to implement numeric integration in Python code for automatic integration.

  • What is the significance of understanding calculus in the context of machine learning?

    -Understanding calculus is crucial as it enables a deeper comprehension of how to fit model parameters to training data, which is fundamental to machine learning.

  • What is the next subject to be covered in the machine learning foundation series after calculus?

    -The next subject to be covered is a combination of probability theory and information theory, which are essential for understanding the probabilistic nature of machine learning models.

  • Why are probability theory and information theory indispensable in machine learning?

    -Probability theory and information theory are indispensable in machine learning because all machine learning models are probabilistic and rely on these subjects to describe and quantify uncertainty.

  • How does the understanding of calculus help in grasping the remaining subjects in the machine learning foundation series?

    -The understanding of calculus, particularly the use of partial derivatives to compute gradients, is critical for fully understanding how gradient descent works, which is a key concept in the subject of machine learning optimization.

  • What are the three thematic segments that make up the calculus subject in the machine learning foundation series?

    -The three thematic segments are: a quick review of essential introductory single variable calculus, a detailed explanation of partial derivatives and their use in determining gradients of machine learning cost functions, and a segment on the integral branch of calculus.

  • How can viewers stay updated with the machine learning foundation series?

    -Viewers can stay updated by subscribing to the channel, signing up for the email newsletter at johncrone.com, connecting on LinkedIn, and following on Twitter.

Outlines
00:00
📚 Final Calculus Video and Resources

The speaker concludes the calculus segment of their machine learning foundation series by recommending external resources for further study. They suggest Dyson Rothedale's book for differential calculus and Zhang Doll's 'Dive into Deep Learning' for integral calculus, along with the visual explanations provided by 'Three Blue One Brown' on YouTube. The segment also recaps the topics covered, including binary classification models, the confusion matrix, the ROC curve, and the use of integral calculus to calculate areas under the curve. The speaker emphasizes the importance of calculus for understanding machine learning optimization and gradient descent, which will be covered in later subjects. They also look forward to the upcoming subject on probability theory and information theory, which are foundational for understanding uncertainty in machine learning models.

Mindmap
Keywords
💡Differential Calculus
Differential calculus is a branch of calculus that deals with the study of rates at which quantities change. It is a fundamental concept in understanding how small changes in one variable can affect another. In the video, it is recommended for further study through Dyson Rothedale's book and the YouTube channel 'three blue one brown'. The video emphasizes its importance in the context of machine learning, particularly in relation to machine learning cost functions and model parameter fitting.
💡Integral Calculus
Integral calculus is another branch of calculus that focuses on the concept of accumulation, or summing up quantities over time. It is used to find areas under curves and volumes of solids, among other things. In the context of the video, integral calculus is crucial for calculating the area under the ROC curve, a key metric in binary classification models. The video suggests resources such as Zhang Doll's 'Dive into Deep Learning' for further exploration.
💡Binary Classification Models
Binary classification models are a type of machine learning model that categorizes data into two classes. They are fundamental in many areas of machine learning and are used to predict outcomes based on input features. The video discusses these models in the context of the confusion matrix and the ROC curve, which are tools used to evaluate the performance of such models.
💡Confusion Matrix
A confusion matrix is a table layout that allows visualization of the performance of an algorithm, especially in binary classification problems. It is a key concept in the video as it is used to create the ROC curve. The confusion matrix is mentioned as 'not so confusing', highlighting its role in the machine learning foundation series.
💡ROC Curve
The Receiver Operating Characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is a critical tool for evaluating the performance of binary classification models. In the video, the ROC curve is introduced as a way to analyze the results of machine learning models, with the area under the curve being a key metric.
💡Numeric Integration
Numeric integration is a type of mathematical technique used to find an approximate value of a definite integral, which is the area under a curve. In the context of the video, numeric integration is used in Python code to automate the process of finding areas under curves, specifically the area under the ROC curve for evaluating machine learning models.
💡Partial Derivatives
Partial derivatives are a concept in calculus that deal with the rate of change of a multivariable function with respect to one variable, while keeping the other variables constant. They are essential in machine learning for determining the gradients of cost functions, which are then used in optimization algorithms. The video series emphasizes the importance of partial derivatives in understanding how model parameters are fitted to training data.
💡Gradient Descent
Gradient descent is an optimization algorithm used to find the values of parameters that minimize a cost function. It is a fundamental concept in machine learning and is directly related to the use of partial derivatives. The video mentions that understanding partial derivatives is critical to fully grasping how gradient descent works, which is a topic to be covered in a later subject of the series.
💡Machine Learning Optimization
Machine learning optimization refers to the process of finding the best model parameters that minimize the cost function. It is a key part of training machine learning models and is closely related to the concepts of partial derivatives and gradient descent. The video series indicates that the upcoming subject will delve into probability theory and information theory, which are essential for understanding machine learning optimization.
💡Probability Theory
Probability theory is a branch of mathematics that deals with the analysis of random phenomena. It is indispensable in machine learning as all models are probabilistic and depend on this theory to describe and quantify uncertainty. The video suggests that probability theory helps in understanding various aspects of life and making better decisions in the face of uncertainty, which is a central theme in the upcoming subject of the series.
💡Information Theory
Information theory is a field of study that deals with the quantification of information and is closely related to probability theory. It plays a crucial role in machine learning by providing a framework for understanding data and its underlying patterns. The video mentions that information theory, along with probability theory, forms the basis for understanding uncertainty in machine learning models.
Highlights

The final calculus video of the Machine Learning Foundation series concludes with recommended external calculus resources.

For differential calculus, Dyson Rothedale's book is recommended, specifically chapter six.

Three Blue One Brown on YouTube offers visual-focused videos on various differential calculus topics.

Appendix 18.5 of Zhang Doll's 'Dive into Deep Learning' is suggested for integral calculus.

The video discusses binary classification models and the confusion matrix.

Introduction to the Receiver Operating Characteristic (ROC) curve.

Exploration of integral calculus to calculate the area under the ROC curve.

Manual calculation of integrals and the use of numeric integration in Python.

The series has covered partial derivatives and integrals, which are essential for understanding machine learning optimization.

Partial derivatives are crucial for understanding gradient descent in machine learning.

The next subject in the series will be probability theory and information theory, which are fundamental to machine learning.

Probability theory helps in understanding and making decisions in the face of uncertainty.

The Machine Learning Foundation series is halfway complete, with four out of eight subjects covered.

The understanding of calculus is key to grasping the remaining subjects in the series.

The upcoming subject will introduce both probability theory and information theory.

The importance of understanding the probabilistic nature of all machine learning models.

Subscription to the channel and signing up for the email newsletter are encouraged to stay updated with the series.

Networking opportunities are available through LinkedIn and Twitter for viewers of the series.

Transcripts

Browse More Related Video

Finding the Area Under the ROC Curve — Topic 91 of Machine Learning Foundations

Calculus II: Partial Derivatives & Integrals — Subject 4 of Machine Learning Foundations

Calculus I: Limits & Derivatives — Subject 3 of Machine Learning Foundations

Calculus Applications – Topic 46 of Machine Learning Foundations

What Integral Calculus Is — Topic 85 of Machine Learning Foundations

2009 AP Calculus AB Free Response #6

Rate This

5.0 / 5 (0 votes)

Thanks for rating:

Related Tags
Machine LearningCalculusDifferentialIntegralPartial DerivativesGradient DescentProbabilistic ModelsUncertaintyOptimizationLearning SeriesMath ResourcesML Foundations