CS229: Machine Learning
It is time to remember the basics!
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include:
- Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- Unsupervised learning (clustering, dimensionality reduction, kernel methods);
- Learning theory (bias/variance tradeoffs, practical advice);
- Reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Links:
- Lecture videos
- Lecture notes
- Course materials
- Main page for the course
- Cheatsheets
Navigational tags: #armknowledgesharing #armcourses
General tags: #machinelearning #supervisedlearning #neuralnetworks #svm #unsupervisedlearning #clustering #kernel #kernel #bias #variance #tradeoff #reinforcementlearning #cheatsheet #data #learning #patternrecognition #datamining
@data_science_weekly
It is time to remember the basics!
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include:
- Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- Unsupervised learning (clustering, dimensionality reduction, kernel methods);
- Learning theory (bias/variance tradeoffs, practical advice);
- Reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Links:
- Lecture videos
- Lecture notes
- Course materials
- Main page for the course
- Cheatsheets
Navigational tags: #armknowledgesharing #armcourses
General tags: #machinelearning #supervisedlearning #neuralnetworks #svm #unsupervisedlearning #clustering #kernel #kernel #bias #variance #tradeoff #reinforcementlearning #cheatsheet #data #learning #patternrecognition #datamining
@data_science_weekly
R2D3 is an experiment in expressing statistical thinking with interactive design.
The site contains several guides:
- A VISUAL INTRODUCTION TO MACHINE LEARNING
- Part 1: A Decision Tree
- Part 2: Bias and Variance
- MISC
- Design in a World where Machines are Learning
- Making Sense of COVID-19
Basically, they try to explain complex concepts using intuitive graphics.
Link: http://www.r2d3.us/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #covid #learning #design #decisiontrees #bias #variance #visualization #eda
@data_science_weekly
The site contains several guides:
- A VISUAL INTRODUCTION TO MACHINE LEARNING
- Part 1: A Decision Tree
- Part 2: Bias and Variance
- MISC
- Design in a World where Machines are Learning
- Making Sense of COVID-19
Basically, they try to explain complex concepts using intuitive graphics.
Link: http://www.r2d3.us/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #covid #learning #design #decisiontrees #bias #variance #visualization #eda
@data_science_weekly
The Illustrated Machine Learning
The idea is to make the complex world of Machine Learning more approachable through clear and concise illustrations.
The goal is to provide a visual aid for students, professionals, and anyone preparing for a technical interview to better understand the underlying concepts of Machine Learning.
Whether you're just starting out in the field or you're a seasoned professional looking to refresh your knowledge, these illustrations will be a valuable resource on your journey to understanding Machine Learning.
- Machine Learning
- Categorization
- Sampling and Resampling
- Bias/Variance
- Supervised Learning
- Unsupervised Learning
- Hyperparameters Tuning
- Machine Learning Engineering
- Introduction
- Before the Project Starts
- Data Collection and Preparation
- Projective Geometry
- Introduction
- Image Formation
- Structure from Motion
- Stereo Reconstruction
- Deep Learning Playbook
Link: https://illustrated-machine-learning.github.io/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #ml #mlsystemdesign #machinelearningsystemdesign #geometry #visualization #illustrated #supervised #unsupervised #dl #deeplearning #bias #variance #biasvariance
@data_science_weekly
The idea is to make the complex world of Machine Learning more approachable through clear and concise illustrations.
The goal is to provide a visual aid for students, professionals, and anyone preparing for a technical interview to better understand the underlying concepts of Machine Learning.
Whether you're just starting out in the field or you're a seasoned professional looking to refresh your knowledge, these illustrations will be a valuable resource on your journey to understanding Machine Learning.
- Machine Learning
- Categorization
- Sampling and Resampling
- Bias/Variance
- Supervised Learning
- Unsupervised Learning
- Hyperparameters Tuning
- Machine Learning Engineering
- Introduction
- Before the Project Starts
- Data Collection and Preparation
- Projective Geometry
- Introduction
- Image Formation
- Structure from Motion
- Stereo Reconstruction
- Deep Learning Playbook
Link: https://illustrated-machine-learning.github.io/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #ml #mlsystemdesign #machinelearningsystemdesign #geometry #visualization #illustrated #supervised #unsupervised #dl #deeplearning #bias #variance #biasvariance
@data_science_weekly