CS 229 ― Machine Learning Cheatsheet
Set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class.
They can (hopefully!) be useful to all future students of this course, as well as to anyone else interested in Machine Learning.
Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #supervisedlearning #unsupervisedlearning #deeplearning #tips #tricks #statistics #probability #calculus
@data_science_weekly
Set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class.
They can (hopefully!) be useful to all future students of this course, as well as to anyone else interested in Machine Learning.
Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #supervisedlearning #unsupervisedlearning #deeplearning #tips #tricks #statistics #probability #calculus
@data_science_weekly
stanford.edu
Teaching - CS 229
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
Statistics and Probability (Khan Academy)
Learn statistics and probability for free - everything you'd want to know about descriptive and inferential statistics:
Unit 1: Analysing categorical data
Unit 2: Displaying and comparing quantitative data
Unit 3: Summarizing quantitative data
Unit 4: Modelling data distributions
Unit 5: Exploring bivariate numerical data
Unit 6: Study design
Unit 7: Probability
Unit 8: Counting, permutations, and combinations
Unit 9: Random variables
Unit 10: Sampling distributions
Unit 11: Confidence intervals
Unit 12: Significance tests (hypothesis testing)
Unit 13: Two-sample inference for the difference between groups
Unit 14: Inference for categorical data (chi-square tests)
Unit 15: Advanced regression (inference and transforming)
Unit 16: Analysis of variance (ANOVA)
Link: https://www.khanacademy.org/math/statistics-probability
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #testing #design #data #abtesting #abtest #probability #ttest
@data_science_weekly
Learn statistics and probability for free - everything you'd want to know about descriptive and inferential statistics:
Unit 1: Analysing categorical data
Unit 2: Displaying and comparing quantitative data
Unit 3: Summarizing quantitative data
Unit 4: Modelling data distributions
Unit 5: Exploring bivariate numerical data
Unit 6: Study design
Unit 7: Probability
Unit 8: Counting, permutations, and combinations
Unit 9: Random variables
Unit 10: Sampling distributions
Unit 11: Confidence intervals
Unit 12: Significance tests (hypothesis testing)
Unit 13: Two-sample inference for the difference between groups
Unit 14: Inference for categorical data (chi-square tests)
Unit 15: Advanced regression (inference and transforming)
Unit 16: Analysis of variance (ANOVA)
Link: https://www.khanacademy.org/math/statistics-probability
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #testing #design #data #abtesting #abtest #probability #ttest
@data_science_weekly
CS109: Probability for Computer Scientists
While the initial foundations of computer science began in the world of discrete mathematics (after all, modern computers are digital in nature), recent years have seen a surge in the use of probability as a tool for the analysis and development of new algorithms and systems. As a result, it is becoming increasingly important for budding computer scientists to understand probability theory, both to provide new perspectives on existing ideas and to help further advance the field in new ways.
CS109: Probability for Computer Scientists starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including particular probability distributions, properties of probabilities, and mathematical tools for analysing probabilities. Finally, the last third of the class will focus on data analysis and machine learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science. This is going to be a great quarter, and we are looking forward to the chance to teach you.
Course Topics
Here are the broad strokes of the course (in approximate order). More information is available on our Schedule page. We cover a very broad set of topics so that you are equipped with the probability and statistics you will see in your future CS studies!
- Counting and probability fundamentals
- Single-dimensional random variables
- Probabilistic models
- Uncertainty theory
- Parameter estimation
- Introduction to machine learning
Links
- Course: https://web.stanford.edu/class/cs109/
- Course Book: https://chrispiech.github.io/probabilityForComputerScientists/en/index.html
- Python for Probability: https://web.stanford.edu/class/archive/cs/cs109/cs109.1238/handouts/python.html
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #probability #stanford #machinelearning #dataanalysis #computerscience #help #mathematics
@data_science_weekly
While the initial foundations of computer science began in the world of discrete mathematics (after all, modern computers are digital in nature), recent years have seen a surge in the use of probability as a tool for the analysis and development of new algorithms and systems. As a result, it is becoming increasingly important for budding computer scientists to understand probability theory, both to provide new perspectives on existing ideas and to help further advance the field in new ways.
CS109: Probability for Computer Scientists starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including particular probability distributions, properties of probabilities, and mathematical tools for analysing probabilities. Finally, the last third of the class will focus on data analysis and machine learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science. This is going to be a great quarter, and we are looking forward to the chance to teach you.
Course Topics
Here are the broad strokes of the course (in approximate order). More information is available on our Schedule page. We cover a very broad set of topics so that you are equipped with the probability and statistics you will see in your future CS studies!
- Counting and probability fundamentals
- Single-dimensional random variables
- Probabilistic models
- Uncertainty theory
- Parameter estimation
- Introduction to machine learning
Links
- Course: https://web.stanford.edu/class/cs109/
- Course Book: https://chrispiech.github.io/probabilityForComputerScientists/en/index.html
- Python for Probability: https://web.stanford.edu/class/archive/cs/cs109/cs109.1238/handouts/python.html
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #probability #stanford #machinelearning #dataanalysis #computerscience #help #mathematics
@data_science_weekly
Mathematics for Machine Learning by Marc Peter Deisenroth and A. Aldo Faisal
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.
For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.
Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Table of Contents
Part I: Mathematical Foundations
1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization
Part II: Central Machine Learning Problems
8. When Models Meet Data
9. Linear Regression
10. Dimensionality Reduction with Principal Component Analysis
11. Density Estimation with Gaussian Mixture Models
12. Classification with Support Vector Machines
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #math #mathematics #maths #calculus #algebra #probability #geometry #optimization #machinelearning #ml
@data_science_weekly
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.
For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.
Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Table of Contents
Part I: Mathematical Foundations
1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization
Part II: Central Machine Learning Problems
8. When Models Meet Data
9. Linear Regression
10. Dimensionality Reduction with Principal Component Analysis
11. Density Estimation with Gaussian Mixture Models
12. Classification with Support Vector Machines
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #math #mathematics #maths #calculus #algebra #probability #geometry #optimization #machinelearning #ml
@data_science_weekly