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Introduction ML - Introduçao a Machine learning

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julianaantunes58

The document outlines an introductory course on machine learning at the University of Texas at Austin, covering topics such as machine learning applications, its history, and how machines learn. It emphasizes the evolution of machine learning, from the early days of human computers to the modern development of AI and machine learning algorithms. The course aims to equip students with programming skills, understanding of key concepts, and practical experience in applying machine learning techniques.

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Introduction to Machine Learning Danna Gurari University of Texas at Austin Spring 2021 https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/CourseContent.html
Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
Key Motivations for Machine Learning Systems that support humans by either improving upon existing human capabilities or providing new capabilities
Problems Solved by Machine Learning Today Spam Detection
Problems Solved by Machine Learning Today Information Retrieval
Problems Solved by Machine Learning Today Recognition (Face) (Speech) (Fraud)
Problems Solved by Machine Learning Today Robotics (Self-driving Vehicles) (Medical Surgery) (Manufacturing)
Problems Solved by Machine Learning Today Recommendation Systems
Problems Solved by Machine Learning Today e.g., recognizing people e.g., shopping without a cashier e.g., self-driving vehicle on Mars Computer Vision Systems
Problems Solved by Machine Learning Today e.g., Amazon’s Echo with Alexa e.g., Google Home Home Virtual Assistants
Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
Origins of ML: Scaling Human Abilities 1613 Human “Computers”: first reference to people who perform calculations towards solving complex problems http://whencomputerswerehuman.djaghe.com/
Origins of ML: Scaling Human Abilities 1613 • e.g., supported NASA space travel in early 1960s Dorothy Vaughn Mary Jackson Miriam Mann Excellent summary: https://en.wikipedia.org/wiki/Human_computer Human “Computers”: first reference to people who perform calculations towards solving complex problems
Origins of ML: Scaling Human Abilities 1613 Human “Computers” 1945 ENIAC (Electronic Numerical Integrator and Computer) created during World War II (could compute 5,000 additions in one second) First programmable machine Human computers became first programmers
Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1950 Turing Test: can ”C” decide whether text responses come from a machine or human Turing Test First programmable machine Alan Turing (1912-1954)
Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1956 Artificial Intelligence First programmable machine “Artificial intelligence” established as a field at a workshop
Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1956 Artificial Intelligence First programmable machine “Artificial intelligence” established as a field at a workshop Workshop Proposal: “… We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer…”
Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 AI researcher Arthur Samuel coins the term “machine learning” as: “Field of study that gives computers the ability to learn without being explicitly programmed.” Machine Learning Artificial Intelligence (machines that do “intelligent” things) Machine Learning (algorithms that “learn” for themselves) 1956
Motivation for Machines that “Learn” • Process for hand-crafted rules: Source: https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ch01.html
Motivation for Machines that “Learn”: Class Task e.g., What rules would you use to answer: “Is a person in the image?”
Motivation for Machines that “Learn” e.g., are these lines parallel?
Motivation for Machines that “Learn” e.g., are these lines parallel?
Motivation for Machines that “Learn” 1. It is hard to hand-craft a complete set of rules 2. We, as humans, may not devise the best rules for a machine since our brains (unconsciously) pre-process the data we sense
Motivation for Machines that “Learn” Should you design rules or use machine learning for these tasks: • Count how many times the letter “F” shows up in this sentence: FINISHED FILES ARE THE RESULT OF YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF YEARS? • Calculate the cost for gasoline on a road trip?
Origins of ML: Rises and Falls of AI/ML Popularity 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 Wave 1 Wave 2 (according to Google Books) Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016 1974 1980 1987 1993 1rst AI Winter 2nd AI Winter
Origins of ML: Rises and Falls of AI/ML Popularity 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 2006 1974 1980 1987 1993 When will be the next fall of AI and ML? 1rst AI Winter 2nd AI Winter
Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
General Idea Excellent reference: https://machinelearningmastery.com/difference-between-algorithm-and-model-in-machine-learning/ An algorithm learns from data patterns that a final model will use to make a prediction
General Idea An algorithm learns from data patterns that a final model will use to make a prediction
Data Types: What a Machine Learns From? • Audio • Input? e.g.,
Data Types: What a Machine Learns From? • Audio • Input? • Images • Input?
Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? Time 1 1 hour Analogous to:
Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? • Text • Input? e.g.,
Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? • Text • Input? • Multi-modal • Input? - combination of the above
Data Types: Many Public Datasets Available • Dataset creation is beyond the scope of this class • We will benefit from other people’s efforts: • Google Dataset Search • Amazon’s AWS datasets • Kaggle datasets • Wikipedia’s list • UC Irvine Machine Learning Repository • Quora.com • Reddit • Dataportals.org • Opendatamonitor.eu • Quandl.com
General Idea An algorithm learns from data patterns that a final model will use to make a prediction
• Unsupervised • Discover patterns/structures in the data How to Learn? • Supervised • Learn to predict for novel cases by studying correct outputs for many data points
How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “cat” What is this?
How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “berimbau” What is this?
How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “yes” Is this email spam?
Types of “Unsupervised” Learning Tasks Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Clustering Anomaly Detection What are real world applications for these types?
Types of “Supervised” Learning Tasks Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Regression (predict continuous value) Classification (predict discrete value) What are real world applications for these types?
Supervised Learning: How to Teach a Machine? Instance-Based Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
Supervised Learning: How to Teach a Machine? Model-Based Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
Supervised Learning: How to Teach a Machine? Model-Based Learning • Goal: learn data distribution in the “real world” • Task: create increasingly complex models to separate x from o • e.g., simple = linear • e.g., more complex = quadratic Figure source: https://medium.com/greyatom/what-is-underfitting-and- overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
Supervised Learning: How to Teach a Machine? Model-Based Learning • Goal: learn data distribution in the “real world” • Modeling: increase vs decrease model’s representational capacity Figure source: https://medium.com/greyatom/what-is-underfitting-and- overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
Supervised Learning: How to Teach a Machine? Online Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
Supervised Learning: How to Teach a Machine? Online Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Why learn incrementally?
Supervised Learning: How to Teach a Machine? Offline Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Algorithm cannot learn incrementally
General Idea An algorithm learns from data patterns that a final model will use to make a prediction
Algorithm Scope for Class: Last 61 Years And More 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 2006 1974 1980 1987 1993 1rst AI Winter 2nd AI Winter
Algorithm Scope: Next 5 Lectures e.g., Linear Regression, Decision Tree, Naïve Bayes, KNN, SVM, Boosting, Bagging, Stacking Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
Algorithm Scope: Middle 4 lectures Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
Algorithm Scope: Other Topics Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
Putting It All Together An algorithm learns from data patterns that a final model will use to make a prediction
Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere An algorithm learns from data patterns that a final model will use to make a prediction
Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere Key Issue: How Fast Will You Get There? (more on this when we discuss CPU and GPU hardware)
Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere Key Issue: Where Will You Go?
Putting It All Together: Where Will You Go? https://www.theverge.com/2015/7/1/8880363/google- apologizes-photos-app-tags-two-black-people-gorillas
Putting It All Together: Where Will You Go? https://www.theverge.com/2015/7/1/8880363/google- apologizes-photos-app-tags-two-black-people-gorillas Why do you think the algorithm made this mistake?
Putting It All Together: Where Will You Go? Two kids bought their mom a Nikon Coolpix S630 digital camera for Mother's Day… when they took portrait pictures of each other, a message flashed across the screen asking, "Did someone blink?" http://content.time.com/time/business/article/0,8599,1954643,00.html
Putting It All Together: Where Will You Go? http://content.time.com/time/business/article/0,8599,1954643,00.html Why do you think the algorithm made this mistake?
Putting It All Together: Where Will You Go? Algorithm identifies men in kitchens as women. Learned this example from given dataset. (Zhao, Wang, Yatskar, Ordonez, Chang, 2017) https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/
Putting It All Together: Where Will You Go? https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/ Algorithm identifies men in kitchens as women. Learned this example from given dataset. (Zhao, Wang, Yatskar, Ordonez, Chang, 2017) Why do you think the algorithm made this mistake?
Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
Introductions Instructor: Danna Gurari Danna: pronounced like “Donna” Gurari: rhymes with Ferrari Interdisciplinary class: share your (1) name, (2) preferred pronouns, and (3) career goal Teaching Assistant: Pei-Chih “Patrick” Chao Office hours: Mon 3-5pm, Thurs 4-5pm Email address: pchao@utexas.edu
Introductions NameCoach: a way to share your name pronunciation in Canvas To record your name: 1. Find NameCoach in Canvas courses page 2. Click on record button to start 3. Check your recording by clicking on play button 1. NameCoach 3. Play 2. Record/Edit
Course Objectives • Understand the key concepts in machine learning: 1. Characterize the process to train and test machine learning algorithms 2. Identify the challenges for designing modern machine learning algorithms that can harness today’s “big” datasets 3. Recognize the strengths and weaknesses of different ways to evaluate machine learning algorithms 4. Critique core and cutting edge machine learning algorithms
Course Objectives • Apply machine learning systems to perform various AI tasks: 1. Develop programming skills by writing code in Python 2. Experiment with machine learning libraries, including scikit-learn and Keras 3. Evaluate machine learning algorithms for tasks in various application domains, including for analyzing text and analyzing images 4. Employ cloud computing resources in order to take advantage of modern hardware and software platforms
Course Objectives • Conduct and communicate original research: 1. Propose a novel research idea (this will be an iterative process) 2. Design and execute experiments to support the proposed idea 3. Write a research paper about the project (and possibly submit it for publication) 4. Present the project to the class
Class Overview • Class website • https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/ • Class objectives, schedule, assignments, and policies • https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/Syllabus /Syllabus.pdf • Grading (from class syllabus):
Q&A: “What are the assignments?” • 5 problem sets (first assignment due next week) • 3 programming assignments • Final project • Pre-proposal • Proposal • Outline • Video Presentation • Peer evaluation • Final project submission (final report, code, and video) • Late policy • Penalized 1% of grade per hour for up to 8 hours • No credit if more than 8 hours late
Q&A: “Do I have the appropriate pre-requisites/background?” • Yes. While there are no pre-requisites, programming experience is strongly recommended. • You will be expected to further develop skills we cover in class on your own • Programming; e.g., Python • Linear algebra; e.g., vector/matrix manipulations • Calculus; e.g., partial derivatives • Probability; e.g., Bayes rule
Q&A: “What are required textbooks?” Required Strongly recommended
Class Format • Mondays = lecture & group discussions • Tuesdays = recorded in-class lab tutorial shared by 10am followed by open, optional Q&A session from 4-5pm
Congratulations! • By taking this class, you receive a gift of: • Thanks to:
What is My “Why” for Teaching You… WHY? To guide and witness you discover more about your potential and your passions HOW? By empowering you to become proficient in one of my passions WHAT? Machine Learning
Today’s Topics • Class logistics • Machine learning applications • History of machine learning • How does a machine learn? • Lab
Figure Credits • https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/9352135210 • https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G • https://www.amazon.com/Deep-Learning-Adaptive-Computation- Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book • https://www.amazon.com/Hidden-Figures-Taraji-P-Henson/dp/B01MU84AWP • https://www.nasa.gov/content/dorothy-vaughan-biography • https://interestingengineering.com/mary-jackson-remembering-nasas-first-black-female-engineer • https://www.bbc.com/news/magazine-39003904 • http://www.pimall.com/nais/pivintage/enic.html • https://en.wikipedia.org/wiki/Turing_test • https://en.wikipedia.org/wiki/Alan_Turing • https://pdfs.semanticscholar.org/d486/9863b5da0fa4ff5707fa972c6e1dc92474f6.pdf • http://brainden.com/line-illusions.htm • https://www.pregnancybirthbaby.org.au/learning-to-crawl • https://www.parents.com/baby/development/intellectual/age-by-age-guide-to-reading-to-your-baby/ • https://www.worthpoint.com/worthopedia/ms-2003-ceramic-purple-car-1861052763

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Introduction ML - Introduçao a Machine learning

  • 1. Introduction to Machine Learning Danna Gurari University of Texas at Austin Spring 2021 https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/CourseContent.html
  • 2. Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
  • 3. Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
  • 4. Key Motivations for Machine Learning Systems that support humans by either improving upon existing human capabilities or providing new capabilities
  • 5. Problems Solved by Machine Learning Today Spam Detection
  • 6. Problems Solved by Machine Learning Today Information Retrieval
  • 7. Problems Solved by Machine Learning Today Recognition (Face) (Speech) (Fraud)
  • 8. Problems Solved by Machine Learning Today Robotics (Self-driving Vehicles) (Medical Surgery) (Manufacturing)
  • 9. Problems Solved by Machine Learning Today Recommendation Systems
  • 10. Problems Solved by Machine Learning Today e.g., recognizing people e.g., shopping without a cashier e.g., self-driving vehicle on Mars Computer Vision Systems
  • 11. Problems Solved by Machine Learning Today e.g., Amazon’s Echo with Alexa e.g., Google Home Home Virtual Assistants
  • 12. Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
  • 13. Origins of ML: Scaling Human Abilities 1613 Human “Computers”: first reference to people who perform calculations towards solving complex problems http://whencomputerswerehuman.djaghe.com/
  • 14. Origins of ML: Scaling Human Abilities 1613 • e.g., supported NASA space travel in early 1960s Dorothy Vaughn Mary Jackson Miriam Mann Excellent summary: https://en.wikipedia.org/wiki/Human_computer Human “Computers”: first reference to people who perform calculations towards solving complex problems
  • 15. Origins of ML: Scaling Human Abilities 1613 Human “Computers” 1945 ENIAC (Electronic Numerical Integrator and Computer) created during World War II (could compute 5,000 additions in one second) First programmable machine Human computers became first programmers
  • 16. Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1950 Turing Test: can ”C” decide whether text responses come from a machine or human Turing Test First programmable machine Alan Turing (1912-1954)
  • 17. Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1956 Artificial Intelligence First programmable machine “Artificial intelligence” established as a field at a workshop
  • 18. Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 1956 Artificial Intelligence First programmable machine “Artificial intelligence” established as a field at a workshop Workshop Proposal: “… We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer…”
  • 19. Origins of ML: Conceptual Framework 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 AI researcher Arthur Samuel coins the term “machine learning” as: “Field of study that gives computers the ability to learn without being explicitly programmed.” Machine Learning Artificial Intelligence (machines that do “intelligent” things) Machine Learning (algorithms that “learn” for themselves) 1956
  • 20. Motivation for Machines that “Learn” • Process for hand-crafted rules: Source: https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ch01.html
  • 21. Motivation for Machines that “Learn”: Class Task e.g., What rules would you use to answer: “Is a person in the image?”
  • 22. Motivation for Machines that “Learn” e.g., are these lines parallel?
  • 23. Motivation for Machines that “Learn” e.g., are these lines parallel?
  • 24. Motivation for Machines that “Learn” 1. It is hard to hand-craft a complete set of rules 2. We, as humans, may not devise the best rules for a machine since our brains (unconsciously) pre-process the data we sense
  • 25. Motivation for Machines that “Learn” Should you design rules or use machine learning for these tasks: • Count how many times the letter “F” shows up in this sentence: FINISHED FILES ARE THE RESULT OF YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF YEARS? • Calculate the cost for gasoline on a road trip?
  • 26. Origins of ML: Rises and Falls of AI/ML Popularity 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 Wave 1 Wave 2 (according to Google Books) Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016 1974 1980 1987 1993 1rst AI Winter 2nd AI Winter
  • 27. Origins of ML: Rises and Falls of AI/ML Popularity 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 2006 1974 1980 1987 1993 When will be the next fall of AI and ML? 1rst AI Winter 2nd AI Winter
  • 28. Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
  • 29. General Idea Excellent reference: https://machinelearningmastery.com/difference-between-algorithm-and-model-in-machine-learning/ An algorithm learns from data patterns that a final model will use to make a prediction
  • 30. General Idea An algorithm learns from data patterns that a final model will use to make a prediction
  • 31. Data Types: What a Machine Learns From? • Audio • Input? e.g.,
  • 32. Data Types: What a Machine Learns From? • Audio • Input? • Images • Input?
  • 33. Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? Time 1 1 hour Analogous to:
  • 34. Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? • Text • Input? e.g.,
  • 35. Data Types: What a Machine Learns From? • Audio • Input? • Images • Input? • Video • Input? • Text • Input? • Multi-modal • Input? - combination of the above
  • 36. Data Types: Many Public Datasets Available • Dataset creation is beyond the scope of this class • We will benefit from other people’s efforts: • Google Dataset Search • Amazon’s AWS datasets • Kaggle datasets • Wikipedia’s list • UC Irvine Machine Learning Repository • Quora.com • Reddit • Dataportals.org • Opendatamonitor.eu • Quandl.com
  • 37. General Idea An algorithm learns from data patterns that a final model will use to make a prediction
  • 38. • Unsupervised • Discover patterns/structures in the data How to Learn? • Supervised • Learn to predict for novel cases by studying correct outputs for many data points
  • 39. How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “cat” What is this?
  • 40. How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “berimbau” What is this?
  • 41. How to Learn? • Unsupervised • No label given for training data • Supervised • Label given for training data: e.g., “yes” Is this email spam?
  • 42. Types of “Unsupervised” Learning Tasks Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Clustering Anomaly Detection What are real world applications for these types?
  • 43. Types of “Supervised” Learning Tasks Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Regression (predict continuous value) Classification (predict discrete value) What are real world applications for these types?
  • 44. Supervised Learning: How to Teach a Machine? Instance-Based Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
  • 45. Supervised Learning: How to Teach a Machine? Model-Based Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
  • 46. Supervised Learning: How to Teach a Machine? Model-Based Learning • Goal: learn data distribution in the “real world” • Task: create increasingly complex models to separate x from o • e.g., simple = linear • e.g., more complex = quadratic Figure source: https://medium.com/greyatom/what-is-underfitting-and- overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
  • 47. Supervised Learning: How to Teach a Machine? Model-Based Learning • Goal: learn data distribution in the “real world” • Modeling: increase vs decrease model’s representational capacity Figure source: https://medium.com/greyatom/what-is-underfitting-and- overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
  • 48. Supervised Learning: How to Teach a Machine? Online Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron
  • 49. Supervised Learning: How to Teach a Machine? Online Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Why learn incrementally?
  • 50. Supervised Learning: How to Teach a Machine? Offline Learning Hands-on Machine Learning with Scikit-Learn & TensorFlow, Aurelien Geron Algorithm cannot learn incrementally
  • 51. General Idea An algorithm learns from data patterns that a final model will use to make a prediction
  • 52. Algorithm Scope for Class: Last 61 Years And More 1613 Human “Computers” 1945 First programmable machine Turing Test & Artificial Intelligence 1959 Machine Learning 1956 2006 1974 1980 1987 1993 1rst AI Winter 2nd AI Winter
  • 53. Algorithm Scope: Next 5 Lectures e.g., Linear Regression, Decision Tree, Naïve Bayes, KNN, SVM, Boosting, Bagging, Stacking Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
  • 54. Algorithm Scope: Middle 4 lectures Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
  • 55. Algorithm Scope: Other Topics Ian Goodfellow, Yoshua Bengio, and Aaron Courville; Deep Learning, 2016.
  • 56. Putting It All Together An algorithm learns from data patterns that a final model will use to make a prediction
  • 57. Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere An algorithm learns from data patterns that a final model will use to make a prediction
  • 58. Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere Key Issue: How Fast Will You Get There? (more on this when we discuss CPU and GPU hardware)
  • 59. Putting It All Together: Analogous to a Love Story of Partnering Up and Road Tripping Somewhere Key Issue: Where Will You Go?
  • 60. Putting It All Together: Where Will You Go? https://www.theverge.com/2015/7/1/8880363/google- apologizes-photos-app-tags-two-black-people-gorillas
  • 61. Putting It All Together: Where Will You Go? https://www.theverge.com/2015/7/1/8880363/google- apologizes-photos-app-tags-two-black-people-gorillas Why do you think the algorithm made this mistake?
  • 62. Putting It All Together: Where Will You Go? Two kids bought their mom a Nikon Coolpix S630 digital camera for Mother's Day… when they took portrait pictures of each other, a message flashed across the screen asking, "Did someone blink?" http://content.time.com/time/business/article/0,8599,1954643,00.html
  • 63. Putting It All Together: Where Will You Go? http://content.time.com/time/business/article/0,8599,1954643,00.html Why do you think the algorithm made this mistake?
  • 64. Putting It All Together: Where Will You Go? Algorithm identifies men in kitchens as women. Learned this example from given dataset. (Zhao, Wang, Yatskar, Ordonez, Chang, 2017) https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/
  • 65. Putting It All Together: Where Will You Go? https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women/ Algorithm identifies men in kitchens as women. Learned this example from given dataset. (Zhao, Wang, Yatskar, Ordonez, Chang, 2017) Why do you think the algorithm made this mistake?
  • 66. Today’s Topics • Machine learning applications • History of machine learning • How does a machine learn? • Class logistics • Lab
  • 67. Introductions Instructor: Danna Gurari Danna: pronounced like “Donna” Gurari: rhymes with Ferrari Interdisciplinary class: share your (1) name, (2) preferred pronouns, and (3) career goal Teaching Assistant: Pei-Chih “Patrick” Chao Office hours: Mon 3-5pm, Thurs 4-5pm Email address: pchao@utexas.edu
  • 68. Introductions NameCoach: a way to share your name pronunciation in Canvas To record your name: 1. Find NameCoach in Canvas courses page 2. Click on record button to start 3. Check your recording by clicking on play button 1. NameCoach 3. Play 2. Record/Edit
  • 69. Course Objectives • Understand the key concepts in machine learning: 1. Characterize the process to train and test machine learning algorithms 2. Identify the challenges for designing modern machine learning algorithms that can harness today’s “big” datasets 3. Recognize the strengths and weaknesses of different ways to evaluate machine learning algorithms 4. Critique core and cutting edge machine learning algorithms
  • 70. Course Objectives • Apply machine learning systems to perform various AI tasks: 1. Develop programming skills by writing code in Python 2. Experiment with machine learning libraries, including scikit-learn and Keras 3. Evaluate machine learning algorithms for tasks in various application domains, including for analyzing text and analyzing images 4. Employ cloud computing resources in order to take advantage of modern hardware and software platforms
  • 71. Course Objectives • Conduct and communicate original research: 1. Propose a novel research idea (this will be an iterative process) 2. Design and execute experiments to support the proposed idea 3. Write a research paper about the project (and possibly submit it for publication) 4. Present the project to the class
  • 72. Class Overview • Class website • https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/ • Class objectives, schedule, assignments, and policies • https://www.ischool.utexas.edu/~dannag/Courses/IntroToMachineLearning/Syllabus /Syllabus.pdf • Grading (from class syllabus):
  • 73. Q&A: “What are the assignments?” • 5 problem sets (first assignment due next week) • 3 programming assignments • Final project • Pre-proposal • Proposal • Outline • Video Presentation • Peer evaluation • Final project submission (final report, code, and video) • Late policy • Penalized 1% of grade per hour for up to 8 hours • No credit if more than 8 hours late
  • 74. Q&A: “Do I have the appropriate pre-requisites/background?” • Yes. While there are no pre-requisites, programming experience is strongly recommended. • You will be expected to further develop skills we cover in class on your own • Programming; e.g., Python • Linear algebra; e.g., vector/matrix manipulations • Calculus; e.g., partial derivatives • Probability; e.g., Bayes rule
  • 75. Q&A: “What are required textbooks?” Required Strongly recommended
  • 76. Class Format • Mondays = lecture & group discussions • Tuesdays = recorded in-class lab tutorial shared by 10am followed by open, optional Q&A session from 4-5pm
  • 77. Congratulations! • By taking this class, you receive a gift of: • Thanks to:
  • 78. What is My “Why” for Teaching You… WHY? To guide and witness you discover more about your potential and your passions HOW? By empowering you to become proficient in one of my passions WHAT? Machine Learning
  • 79. Today’s Topics • Class logistics • Machine learning applications • History of machine learning • How does a machine learn? • Lab
  • 80. Figure Credits • https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/9352135210 • https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G • https://www.amazon.com/Deep-Learning-Adaptive-Computation- Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book • https://www.amazon.com/Hidden-Figures-Taraji-P-Henson/dp/B01MU84AWP • https://www.nasa.gov/content/dorothy-vaughan-biography • https://interestingengineering.com/mary-jackson-remembering-nasas-first-black-female-engineer • https://www.bbc.com/news/magazine-39003904 • http://www.pimall.com/nais/pivintage/enic.html • https://en.wikipedia.org/wiki/Turing_test • https://en.wikipedia.org/wiki/Alan_Turing • https://pdfs.semanticscholar.org/d486/9863b5da0fa4ff5707fa972c6e1dc92474f6.pdf • http://brainden.com/line-illusions.htm • https://www.pregnancybirthbaby.org.au/learning-to-crawl • https://www.parents.com/baby/development/intellectual/age-by-age-guide-to-reading-to-your-baby/ • https://www.worthpoint.com/worthopedia/ms-2003-ceramic-purple-car-1861052763