Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course 3: Machine Learning Fundamentals........ 34 2.2 Bram Dedrick............................. 36 2.2.1 Online Courses........................ 36 2.2.2 Beginning the Class..................... 37 2.2.3 Machine Learning Courses Offered in Other Schools... 37 2.3 Tony Ferenzi............................. 39 2.3.1 Robotics: Vision Intelligence and Machine Learning... 41 2.3.2 Machine Learning...................... 41 2.3.3 Machine Learning Fundamentals.............. 41 2.3.4 Machine Learning...................... 42 2.3.5 Data Science: Machine learning.............. 42 2.4 William Golden............................ 43 2.4.1 course overview........................ 43 2.4.2 Conclusion.......................... 45 2.5 Yuan Hong.............................. 46 2.6 Easton Jensen............................. 49 2.6.1 Overview........................... 49 2.6.2 Course 1............................ 49 2.6.3 Course 2............................ 49 2.6.4 Course 3............................ 50 2.6.5 Course 4............................ 50 2.6.6 Why Take The Course.................... 51 2.7 Rodrigo Martinez........................... 52 2.8 Matt Morrical............................. 54 2.9 Ella Nelson.............................. 55 2.10 Koichi Okazaki............................ 56 CONTENTS v 2.10.1 Deep Learning Nanodegree Program from Udacity.... 56 2.10.2 At the end.......................... 59 2.11 Jakob Orel.............................. 60 2.11.1 Coursera........................... 60 2.11.2 EdX.............................. 61 2.11.3 Argonne National Laboratory................ 61 2.11.4 Takeaway........................... 62 2.12 Marcellus Parks............................ 63 2.13 Lydia Sanchez............................. 65 2.14 Tiff Serra-Pichardo.......................... 70 2.15 Austin Stala.............................. 71 2.15.1 Machine Learning Background............... 71 2.15.2 Lesson Resources....................... 74 2.15.3 Conclusion.......................... 74 2.16 Nicole Trenholm........................... 76 2.17 Maddy Weaver............................ 79 2.18 Peter Weber.............................. 83 3 Algorithms: how machine learning works 85 3.1 Sheldon Branch............................ 85 3.2 Bram Dedrick............................. 87 3.3 Tony Ferenzi............................. 89 3.4 William Golden............................ 92 3.4.1 A brief introduction..................... 92 3.4.2 Programs for Computer Learning.............. 92 3.4.3 Good Terms to Know.................... 92 3.4.4 Data mining in use...................... 93 3.4.5 Algorithms.......................... 94 3.4.6 Creating Speech Recognition................ 95 3.4.7 Conclusion.......................... 96 3.5 Yuan Hong.............................. 97 vi CONTENTS 3.6 Easton Jensen............................. 99 3.7 Rodrigo Martinez........................... 100 3.8 Matt Morrical............................. 102 3.9 Ella Nelson.............................. 103 3.10 Koichi Okazaki............................ 104 3.10.1 Facial Recognition...................... 104 3.11 Jakob Orel.............................. 106 3.12 Marcellus Parks............................ 108 3.13 Lydia Sanchez............................. 110 3.14 Tiff Serra-Pichardo.......................... 113 3.14.1 Artificial Neural Networks and How They Work..... 113 3.15 Austin Stala.............................. 114 3.16 Nicole Trenholm........................... 116 3.17 Maddy Weaver............................ 118 3.17.1 Random Forest........................ 118 3.18 Peter Weber.............................. 121 3.18.1 Neural Networks....................... 121 4 Authors: Who we are and how we learn 123 4.1 Sheldon Branch............................ 123 4.2 Bram Dedrick............................. 124 4.3 Tony Ferenzi............................. 125 4.4 William Golden............................ 126 4.5 Yuan Hong.............................. 127 4.6 Easton Jensen............................. 128 4.7 Rodrigo Martinez........................... 129 4.8 Matt Morrical............................. 130 4.9 Ella Nelson.............................. 131 4.10 Koichi Okazaki............................ 132 4.11 Jakob Orel.............................. 133 4.12 Marcellus Parks............................ 134 CONTENTS vii 4.13 Lydia Sanchez............................. 135 4.14 Tiff Serra-Pichardo.......................... 136 4.15 Austin Stala.............................. 137 4.16 Nicole Trenholm........................... 138 4.17 Maddy Weaver............................ 139 4.18 Peter Weber.............................. 140 4.18.1 Best learning experiences.................. 140 viii CONTENTS Chapter 1 Applications: where machine learning is helping 1.1 Sheldon Branch Sheldon Branch CSC131 9/3/13 Machine Learning in the Medical Field The medical field is facing a huge problem, one that affects over 14 million people every year. This problem is cancer. Because of our lack of knowledge, doctors are often unable to reliably identify cancer. Researchers at The Johns Hop- kins Hospital in Baltimore reviewed tissue samples from 6,000 cancer patients across the country. They found one out of every 71 cases was misdiagnosed; for example, a biopsy was labeled cancerous when it was not. And up to one out of five cancer cases was misclassified. Human error in the medical field is responsible for hundreds of thousands of deaths each year. So where does ma- chine learning fall into all of this? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learn- ing focuses on the development of computer programs that can access data and use it learn for themselves. By using algorithms to build models that uncover connections, doctors can make better decisions without human intervention. This innovation allows calculations and diagnoses to be made without human error. Healthcare involves a lot of prediction tasks. Such tasks are perfect for an artificial intelligence whose sole purpose is to predict. Regina Barzilay is an AI pioneer who leads a team at MIT. In 2014, she was diagnosed with breast cancer. After her initial diagnosis, she had a mammogram and the doctors said that her cancer was very small. Next, the doctors did an MRI which reported that Reginas cancer was very widespread. The doctors then needed to do a biopsy on Regina, which elucidated that the MRI showed a false positive. After her treatment, her past mammograms were reviewed and small spots of cancer 1 were found on the scans as early as two years prior to her diagnosis. Using a machine to predict cancer instead of doing painful and expensive procedures is much more efficient. Her diagnosis and treatment fueled her desire to use her knowledge of artificial intelligence and machine learning to prevent such mistakes and improve the accuracy of cancer diagnoses. Regina aims to utilize her computer science knowledge to create an AI that can recognize patterns in medical images and doctors electronic notes in an attempt to catch cancer earlier and avoid overusing invasive treatments. A few years ago, Researchers in Brazil studied the brains of expert radiologist in order to understand how they reached their diagnoses. The researchers wanted to know if the seasoned diagnosticians were applying a mental rule book to the images, or if they applied pattern recognition. Twenty-five radiologists were asked to evaluate X-rays of the lung while inside MRI machines that could track their brain activity. Some of the X-rays contained common encounters, such as a palm shaped shadow of pneumonia or the dull, opaque wall of fluid that accumulated behind the lining of the lung. Others showed lines of drawings of animals. The final group had the outlines of letters of the alphabet. The radiologists were shown these images in a random order and then asked to call out the name of the lesion,