title
Introduction

description

detail
{'title': 'Introduction', 'heatmap': [{'end': 1260.468, 'start': 1222.111, 'weight': 0.71}, {'end': 1319.036, 'start': 1293.055, 'weight': 0.781}, {'end': 1458.317, 'start': 1423.737, 'weight': 1}], 'summary': 'Provides an introduction to an 8-week machine learning course, covering topics such as linear regression, decision trees, neural networks, and ensemble learning. it also explores the history and evolution of machine learning, emphasizing its impact and applications across industries.', 'chapters': [{'end': 118.449, 'segs': [{'end': 74.492, 'src': 'embed', 'start': 18.834, 'weight': 0, 'content': [{'end': 21.535, 'text': 'Good morning, I am Sudeshna Sarkar.', 'start': 18.834, 'duration': 2.701}, {'end': 24.636, 'text': 'Today we start the first lecture on machine learning.', 'start': 21.615, 'duration': 3.021}, {'end': 28.537, 'text': 'This is module 1, part a.', 'start': 24.656, 'duration': 3.881}, {'end': 34.319, 'text': 'Today we will introduce machine learning, go through the basics of the course,', 'start': 28.537, 'duration': 5.782}, {'end': 43.962, 'text': 'discuss the brief history of machine learning and discuss what learning is about and some simple applications of machine learning.', 'start': 34.319, 'duration': 9.643}, {'end': 49.317, 'text': 'First, this is the overview of the course.', 'start': 47.196, 'duration': 2.121}, {'end': 54.358, 'text': 'The course is over 8 weeks and will have 8 modules.', 'start': 49.677, 'duration': 4.681}, {'end': 56.819, 'text': 'The first module is introduction.', 'start': 54.919, 'duration': 1.9}, {'end': 61.641, 'text': 'In the second module, we will discuss about linear regression and decision trees.', 'start': 57.419, 'duration': 4.222}, {'end': 66.024, 'text': 'Third module, Instance Based Learning and Feature Selection.', 'start': 62.481, 'duration': 3.543}, {'end': 69.247, 'text': 'Fourth module, Probability and Base Learning.', 'start': 66.605, 'duration': 2.642}, {'end': 71.99, 'text': 'Fifth module, Support Vector Machines.', 'start': 69.848, 'duration': 2.142}, {'end': 74.492, 'text': 'Sixth module, Dural Networks.', 'start': 72.691, 'duration': 1.801}], 'summary': 'Introduction to 8-week machine learning course with 8 modules.', 'duration': 55.658, 'max_score': 18.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY18834.jpg'}, {'end': 118.449, 'src': 'embed', 'start': 92.15, 'weight': 1, 'content': [{'end': 99.739, 'text': 'In the next lecture, we will have discuss about different types of learning supervised, unsupervised, etcetera.', 'start': 92.15, 'duration': 7.589}, {'end': 106.218, 'text': 'Then we will have the third module where we will talk about hypothesis space and inductive bias.', 'start': 101.013, 'duration': 5.205}, {'end': 113.004, 'text': 'Following this we will talk about evaluation, training and test set and cross validation.', 'start': 106.758, 'duration': 6.246}, {'end': 118.449, 'text': 'First I will like to start with a brief history of machine learning.', 'start': 113.604, 'duration': 4.845}], 'summary': 'Next lecture: discuss supervised, unsupervised learning, hypothesis space, evaluation, training, test set, and cross validation, with a brief history of machine learning.', 'duration': 26.299, 'max_score': 92.15, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY92150.jpg'}], 'start': 18.834, 'title': 'An 8-week course on machine learning', 'summary': 'Introduces an 8-week course on machine learning, covering linear regression, decision trees, instance-based learning, feature selection, probability, support vector machines, neural networks, computational learning theory, ensemble learning, and clustering, with a brief history of machine learning and an outline of the course structure.', 'chapters': [{'end': 118.449, 'start': 18.834, 'title': 'Introduction to machine learning', 'summary': 'Introduces an 8-week course on machine learning, covering topics such as linear regression, decision trees, instance-based learning, feature selection, probability, support vector machines, neural networks, computational learning theory, ensemble learning, and clustering, with a brief history of machine learning and an outline of the course structure.', 'duration': 99.615, 'highlights': ['The course is over 8 weeks and will have 8 modules, covering various topics such as linear regression, decision trees, support vector machines, and neural networks.', 'The first module will include an introduction to machine learning, with subsequent modules focusing on different types of learning, hypothesis space, inductive bias, evaluation, training, test sets, and cross validation.', 'The chapter provides a brief history of machine learning as part of the introductory lecture on machine learning.']}], 'duration': 99.615, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY18834.jpg', 'highlights': ['The course is over 8 weeks and will have 8 modules, covering various topics such as linear regression, decision trees, support vector machines, and neural networks.', 'The first module will include an introduction to machine learning, with subsequent modules focusing on different types of learning, hypothesis space, inductive bias, evaluation, training, test sets, and cross validation.', 'The chapter provides a brief history of machine learning as part of the introductory lecture on machine learning.']}, {'end': 298.37, 'segs': [{'end': 152.596, 'src': 'embed', 'start': 119.955, 'weight': 0, 'content': [{'end': 133.804, 'text': 'A machine that is intellectually capable as much as humans has always fired the imagination of writers and also the early computer scientists,', 'start': 119.955, 'duration': 13.849}, {'end': 137.867, 'text': 'who were excited about artificial intelligence and machine learning.', 'start': 133.804, 'duration': 4.063}, {'end': 145.053, 'text': 'But the first machine learning system was developed in the 1950s.', 'start': 138.871, 'duration': 6.182}, {'end': 152.596, 'text': 'In 1952 Arthur Samuel was at IBM, he developed a program for playing checkers.', 'start': 145.213, 'duration': 7.383}], 'summary': 'First machine learning system developed in 1952 for playing checkers.', 'duration': 32.641, 'max_score': 119.955, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY119955.jpg'}, {'end': 205.947, 'src': 'embed', 'start': 179.156, 'weight': 1, 'content': [{'end': 190.863, 'text': 'Samuel coined the term machine learning and he defined learning as a field of study that gives computers the ability without being explicitly programmed.', 'start': 179.156, 'duration': 11.707}, {'end': 198.302, 'text': 'In 1957 Rosenblatt proposed the perceptron.', 'start': 192.277, 'duration': 6.025}, {'end': 201.724, 'text': 'Perceptron is the simple neural network unit.', 'start': 198.742, 'duration': 2.982}, {'end': 205.947, 'text': 'It was a very exciting discovery at that time,', 'start': 202.265, 'duration': 3.682}], 'summary': 'In 1957, rosenblatt proposed the perceptron, a simple neural network unit, marking an exciting discovery in the field of machine learning.', 'duration': 26.791, 'max_score': 179.156, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY179156.jpg'}, {'end': 265.561, 'src': 'embed', 'start': 229.626, 'weight': 2, 'content': [{'end': 238.634, 'text': 'But, after 3 years Widrow and Hoff came up with the delta learning rule that is used for learning perceptrons.', 'start': 229.626, 'duration': 9.008}, {'end': 245.52, 'text': 'It was used as a procedure for training perceptron, it is also known as the least square problem.', 'start': 238.914, 'duration': 6.606}, {'end': 250.905, 'text': 'The combination of these ideas created a good linear classifier.', 'start': 246.441, 'duration': 4.464}, {'end': 265.561, 'text': 'However, The work along this line suffered a setback when Minsky in 1969 came up with the limitations of perceptron.', 'start': 251.625, 'duration': 13.936}], 'summary': 'Widrow and hoff developed the delta learning rule for training perceptrons, leading to a good linear classifier, but minsky highlighted its limitations in 1969.', 'duration': 35.935, 'max_score': 229.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY229626.jpg'}], 'start': 119.955, 'title': 'History of machine learning', 'summary': 'Traces the history of machine learning from the 1950s, highlighting key developments such as the creation of the first machine learning system by arthur samuel, the introduction of the perceptron by rosenblatt, and the setback faced by neural network research in the late 1960s.', 'chapters': [{'end': 298.37, 'start': 119.955, 'title': 'History of machine learning', 'summary': 'Traces the history of machine learning from the 1950s, highlighting key developments such as the creation of the first machine learning system by arthur samuel, the introduction of the perceptron by rosenblatt, and the setback faced by neural network research in the late 1960s.', 'duration': 178.415, 'highlights': ["Arthur Samuel developed the first machine learning system at IBM for playing checkers in 1952, enabling the program to learn and improve its gameplay over time. Arthur Samuel developed a machine learning program for playing checkers in 1952 at IBM, which observed game positions and learned a model for better moves, demonstrating the system's ability to improve over time with experience.", "Rosenblatt introduced the perceptron in 1957, a significant discovery in the field of neural networks, but Minsky's work in 1969 revealed the limitations of perceptron, leading to a dormant period in neural network research until the 1980s. Rosenblatt's introduction of the perceptron in 1957 marked a significant milestone in neural network research, but Minsky's 1969 findings on the limitations of perceptron led to a period of dormancy in neural network research until the 1980s.", "Widrow and Hoff developed the delta learning rule in the 1950s, which served as a training procedure for perceptrons and contributed to the creation of a good linear classifier. Widrow and Hoff's development of the delta learning rule in the 1950s provided a training procedure for perceptrons and contributed to the creation of an effective linear classifier."]}], 'duration': 178.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY119955.jpg', 'highlights': ['Arthur Samuel developed the first machine learning system at IBM for playing checkers in 1952, enabling the program to learn and improve its gameplay over time.', "Rosenblatt introduced the perceptron in 1957, a significant discovery in the field of neural networks, but Minsky's work in 1969 revealed the limitations of perceptron, leading to a dormant period in neural network research until the 1980s.", 'Widrow and Hoff developed the delta learning rule in the 1950s, which served as a training procedure for perceptrons and contributed to the creation of a good linear classifier.']}, {'end': 492.702, 'segs': [{'end': 414.48, 'src': 'embed', 'start': 298.37, 'weight': 4, 'content': [{'end': 303.253, 'text': 'good old fashioned artificial intelligence those types of learning algorithms were developed.', 'start': 298.37, 'duration': 4.883}, {'end': 308.877, 'text': 'Concept induction was worked on and then J.', 'start': 303.754, 'duration': 5.123}, {'end': 314.161, 'text': 'R Quinlan in 1986 came up with decision tree learning.', 'start': 308.877, 'duration': 5.284}, {'end': 317.203, 'text': 'specifically the ID3 algorithm.', 'start': 314.721, 'duration': 2.482}, {'end': 328.369, 'text': 'It was also released as a software and it had simplistic rules contrary to the black box of neural networks and it became quite popular.', 'start': 317.563, 'duration': 10.806}, {'end': 337.635, 'text': 'After ID3, many alternatives or improvement to ID3 were developed, such as CART regression trees,', 'start': 329.23, 'duration': 8.405}, {'end': 341.778, 'text': 'and it is still one of the very popular topics in machine learning.', 'start': 337.635, 'duration': 4.143}, {'end': 348.305, 'text': 'During this time, symbolic natural language processing also became very popular.', 'start': 342.879, 'duration': 5.426}, {'end': 354.072, 'text': 'In 1980s, advanced decision trees and rule learning were developed.', 'start': 349.206, 'duration': 4.866}, {'end': 357.736, 'text': 'Learning, planning, problem solving was there.', 'start': 354.592, 'duration': 3.144}, {'end': 361.64, 'text': 'At the same time, there was a resurgence of neural network.', 'start': 358.016, 'duration': 3.624}, {'end': 374.416, 'text': 'The intuition of multi-layer perceptron was suggested by Verbos in 1981 and neural network specific back propagation algorithm was developed.', 'start': 362.802, 'duration': 11.614}, {'end': 380.794, 'text': "Back propagation is the key ingredient of today's neural network architectures.", 'start': 375.47, 'duration': 5.324}, {'end': 386.097, 'text': 'With those ideas, neural network research became popular again,', 'start': 381.494, 'duration': 4.603}, {'end': 396.424, 'text': 'and there was an acceleration in 1985-86 when neural network researchers presented the idea of MLP, that is, multilayer perceptron,', 'start': 386.097, 'duration': 10.327}, {'end': 398.305, 'text': 'with practical BP training.', 'start': 396.424, 'duration': 1.881}, {'end': 405.15, 'text': 'Rumelhart, Hinton, Williams, Hetch, Nielsen were some of the scientists who worked in this area.', 'start': 398.906, 'duration': 6.244}, {'end': 414.48, 'text': "During this time, theoretical framework of machine learning was also presented, Valiant's PAC learning theory,", 'start': 406.391, 'duration': 8.089}], 'summary': 'In the 1980s, ai saw advances in decision trees, neural networks, and natural language processing, leading to a resurgence in neural network research and the introduction of pac learning theory.', 'duration': 116.11, 'max_score': 298.37, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY298370.jpg'}, {'end': 492.702, 'src': 'embed', 'start': 434.052, 'weight': 0, 'content': [{'end': 448.337, 'text': 'It was a machine learning breakthrough and the support vector machines was proposed by Vapnik and Cortes in 1995 and SVM had very strong theoretical standing and empirical results.', 'start': 434.052, 'duration': 14.285}, {'end': 461.723, 'text': 'Then another strong machine learning model was proposed by Freund and Shapire in 1997, which was part of what we called ensembles or boosting,', 'start': 450.376, 'duration': 11.347}, {'end': 471.75, 'text': 'and they came up with a algorithm called AdaBoost, by which they could create a strong classifier from an ensemble of weak classifiers.', 'start': 461.723, 'duration': 10.027}, {'end': 484.18, 'text': 'The kernelized version of SVM was proposed near 2000s, which was able to exploit the knowledge of convex optimization, generalization and kernels.', 'start': 473.257, 'duration': 10.923}, {'end': 492.702, 'text': 'Another ensemble model was explored by Bryman in 2001 that ensembles, multiple decision trees,', 'start': 484.8, 'duration': 7.902}], 'summary': 'Breakthroughs in machine learning: svm in 1995, adaboost in 1997, kernelized svm in 2000s, and ensemble models in 2001.', 'duration': 58.65, 'max_score': 434.052, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY434052.jpg'}], 'start': 298.37, 'title': 'Evolution of ml models', 'summary': 'Discusses the evolution of decision tree learning, including the development of the id3 algorithm in 1986, the resurgence of neural networks in the 1980s, and the development of support vector machines and ensemble models in the 90s and early 2000s, highlighting key advancements and their impact on machine learning research.', 'chapters': [{'end': 357.736, 'start': 298.37, 'title': 'Evolution of decision tree learning', 'summary': 'Discusses the evolution of decision tree learning, starting with the development of the id3 algorithm in 1986 by j. r quinlan, its simplicity compared to neural networks, and its enduring popularity in machine learning, along with the rise of alternative or improved versions like cart regression trees and the increasing prominence of symbolic natural language processing.', 'duration': 59.366, 'highlights': ["J. R Quinlan's development of the ID3 algorithm in 1986, known for its simplistic rules, contrasting the black box nature of neural networks, and gaining significant popularity in machine learning.", 'The subsequent development of alternative or improved versions of ID3, such as CART regression trees, which remain popular in machine learning.', 'The increasing prominence of symbolic natural language processing during the 1980s, marking a significant period in the evolution of decision tree learning and machine learning in general.']}, {'end': 414.48, 'start': 358.016, 'title': 'Resurgence of neural network', 'summary': 'Describes the resurgence of neural networks in the 1980s, with the development of the multi-layer perceptron and back propagation algorithm, leading to an acceleration in neural network research and the presentation of the pac learning theory.', 'duration': 56.464, 'highlights': ["Theoretical framework of machine learning was presented, including Valiant's PAC learning theory.", 'Neural network researchers presented the idea of multilayer perceptron (MLP) with practical back propagation (BP) training, leading to an acceleration in 1985-86.', 'Back propagation algorithm was developed, becoming the key ingredient of modern neural network architectures.', 'The intuition of multi-layer perceptron was suggested by Verbos in 1981, contributing to the resurgence of neural network research.', 'Rumelhart, Hinton, Williams, Hetch, Nielsen were some of the scientists who worked on the practical implementation of MLP with BP training.']}, {'end': 492.702, 'start': 414.48, 'title': 'Evolution of machine learning models', 'summary': 'Discusses the evolution of machine learning models, highlighting the development of support vector machines and ensemble models, such as adaboost and multiple decision trees, in the 90s and early 2000s.', 'duration': 78.222, 'highlights': ['Support Vector Machines proposed by Vapnik and Cortes in 1995 Support Vector Machines proposed by Vapnik and Cortes in 1995 had strong theoretical standing and empirical results, marking a significant machine learning breakthrough.', 'AdaBoost algorithm introduced by Freund and Shapire in 1997 AdaBoost algorithm introduced by Freund and Shapire in 1997 enabled the creation of a strong classifier from an ensemble of weak classifiers, contributing to the development of ensemble models.', 'Kernelized version of SVM leveraging convex optimization and kernels proposed near 2000s The kernelized version of SVM, proposed near 2000s, was able to exploit the knowledge of convex optimization, generalization, and kernels, further advancing the capabilities of support vector machines.', "Exploration of ensemble models by Bryman in 2001, including multiple decision trees Bryman's exploration of ensemble models in 2001, particularly multiple decision trees, contributed to the diversification and advancement of ensemble learning in machine learning."]}], 'duration': 194.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY298370.jpg', 'highlights': ['Support Vector Machines proposed by Vapnik and Cortes in 1995 had strong theoretical standing and empirical results, marking a significant machine learning breakthrough.', 'The kernelized version of SVM, proposed near 2000s, was able to exploit the knowledge of convex optimization, generalization, and kernels, further advancing the capabilities of support vector machines.', 'Exploration of ensemble models by Bryman in 2001, including multiple decision trees, contributed to the diversification and advancement of ensemble learning in machine learning.', 'AdaBoost algorithm introduced by Freund and Shapire in 1997 enabled the creation of a strong classifier from an ensemble of weak classifiers, contributing to the development of ensemble models.', "Theoretical framework of machine learning was presented, including Valiant's PAC learning theory.", 'Back propagation algorithm was developed, becoming the key ingredient of modern neural network architectures.', 'The intuition of multi-layer perceptron was suggested by Verbos in 1981, contributing to the resurgence of neural network research.', 'Neural network researchers presented the idea of multilayer perceptron (MLP) with practical back propagation (BP) training, leading to an acceleration in 1985-86.', "J. R Quinlan's development of the ID3 algorithm in 1986, known for its simplistic rules, contrasting the black box nature of neural networks, and gaining significant popularity in machine learning.", 'The subsequent development of alternative or improved versions of ID3, such as CART regression trees, which remain popular in machine learning.', 'The increasing prominence of symbolic natural language processing during the 1980s, marking a significant period in the evolution of decision tree learning and machine learning in general.']}, {'end': 1097.384, 'segs': [{'end': 625.667, 'src': 'embed', 'start': 524.99, 'weight': 0, 'content': [{'end': 527.652, 'text': 'neural networks are inclined to over fit.', 'start': 524.99, 'duration': 2.662}, {'end': 536.936, 'text': 'But as we come closer today, we see that neural networks are again very much popular.', 'start': 529.673, 'duration': 7.263}, {'end': 546.58, 'text': 'We have a new era in neural network called deep learning and this phrase refers to neural network with many deep layers.', 'start': 537.396, 'duration': 9.184}, {'end': 559.45, 'text': 'This rise of neural network began roughly in 2005, with the conjunction of many different discoveries by people like Hinton, Lecun Bengio,', 'start': 547.74, 'duration': 11.71}, {'end': 561.932, 'text': 'Andrew Ng and other researchers.', 'start': 559.45, 'duration': 2.482}, {'end': 572.019, 'text': 'At the same time, if you look at certain applications where machine learning has come to the public forefront.', 'start': 563.777, 'duration': 8.242}, {'end': 578.22, 'text': 'in 1994 the first self-driving car made a road test.', 'start': 572.019, 'duration': 6.201}, {'end': 583.682, 'text': 'In 1997 Deep Blue beat the world champion Garry Kasparov in the game of chess.', 'start': 578.241, 'duration': 5.441}, {'end': 590.634, 'text': 'In 2009, we have Google building self-driving cars.', 'start': 584.783, 'duration': 5.851}, {'end': 595.884, 'text': 'In 2011, Watson again from IBM won the popular game of GeoBuddy.', 'start': 590.654, 'duration': 5.23}, {'end': 602.231, 'text': 'In 2014, we see human vision surpassed by ML systems.', 'start': 597.507, 'duration': 4.724}, {'end': 604.333, 'text': 'In 2015,,', 'start': 602.391, 'duration': 1.942}, {'end': 617.564, 'text': 'we find that machine translation systems driven by neural networks are very good and they are better than the other statistical machine translation systems.', 'start': 604.333, 'duration': 13.231}, {'end': 625.667, 'text': 'There are certain concepts and certain technology which are making headlines now in machine learning.', 'start': 618.065, 'duration': 7.602}], 'summary': "Neural networks' resurgence led to major ai advancements in various applications, with notable milestones such as self-driving cars and defeating human champions in games.", 'duration': 100.677, 'max_score': 524.99, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY524990.jpg'}, {'end': 909.588, 'src': 'embed', 'start': 825.993, 'weight': 2, 'content': [{'end': 841.726, 'text': 'For example, model can be used for prediction, decision making, or solving tasks.', 'start': 825.993, 'duration': 15.733}, {'end': 860.027, 'text': 'Now, we will discuss a formal definition of machine learning as given by Tom Mitchell.', 'start': 853.459, 'duration': 6.568}, {'end': 867.549, 'text': 'and this is a definition that is followed very popularly.', 'start': 863.087, 'duration': 4.462}, {'end': 909.588, 'text': 'So, Michels definition of machine learning, it says that a computer program is said to from, what does it learn from? It learns from experience.', 'start': 868.57, 'duration': 41.018}], 'summary': 'Machine learning: a computer program learns from experience.', 'duration': 83.595, 'max_score': 825.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY825993.jpg'}], 'start': 493.382, 'title': 'Machine learning evolution and components', 'summary': 'Covers the history of machine learning, including neural networks and deep learning, and discusses components such as tasks, experience data, and performance measures, emphasizing the importance of improving task performance through experience data.', 'chapters': [{'end': 909.588, 'start': 493.382, 'title': 'Evolution of machine learning', 'summary': 'Covers the history of machine learning, from the development of neural networks to their resurgence with deep learning, as well as key milestones in the field such as the advancements in self-driving cars, chess-playing ai, and machine translation systems.', 'duration': 416.206, 'highlights': ['The rise of neural networks in 2005, led by researchers like Hinton, Lecun, and Bengio, marked a new era in machine learning, paving the way for deep learning with many deep layers.', 'In 2015, machine translation systems driven by neural networks surpassed other statistical machine translation systems, showcasing the advancements in language processing.', "The development of machine learning is evident in significant milestones such as the first self-driving car road test in 1994, Deep Blue defeating Garry Kasparov in 1997, and Google's self-driving cars in 2009, demonstrating the increasing applications of machine learning in real-world scenarios.", 'The formal definition of machine learning by Tom Mitchell emphasizes the concept of learning from experience, where computer systems automatically improve with experience, and the models built can be used for prediction, decision making, and solving tasks.']}, {'end': 1097.384, 'start': 910.669, 'title': 'Understanding machine learning components', 'summary': 'Discusses machine learning components, including tasks, experience data, and performance measures, and emphasizes the importance of improving performance on tasks through experience data.', 'duration': 186.715, 'highlights': ['The components of a learning algorithm are the task behavior, the experience or data used for improving the experience at the task, and a measure of improvement, such as increasing accuracy in prediction.', 'Machine learning is about using experience data from past problem-solving data to improve performance on tasks in a class of tasks as measured by a performance measure.', 'Tasks in a class of tasks T are evaluated by performance measure P, and the machine is said to learn tasks in T if its performance at tasks improves with experience E.']}], 'duration': 604.002, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY493382.jpg', 'highlights': ['The rise of neural networks in 2005 marked a new era in machine learning, paving the way for deep learning with many deep layers.', 'In 2015, machine translation systems driven by neural networks surpassed other statistical machine translation systems, showcasing advancements in language processing.', 'The formal definition of machine learning by Tom Mitchell emphasizes the concept of learning from experience, where computer systems automatically improve with experience, and the models built can be used for prediction, decision making, and solving tasks.', "The development of machine learning is evident in significant milestones such as the first self-driving car road test in 1994, Deep Blue defeating Garry Kasparov in 1997, and Google's self-driving cars in 2009, demonstrating the increasing applications of machine learning in real-world scenarios."]}, {'end': 1422.251, 'segs': [{'end': 1122.833, 'src': 'embed', 'start': 1098.405, 'weight': 1, 'content': [{'end': 1108.768, 'text': 'or you might want to have new skills to the agent, which it did not earlier process, or improve efficiency of problem solving.', 'start': 1098.405, 'duration': 10.363}, {'end': 1112.47, 'text': 'corresponding to this, you can define the performance measure.', 'start': 1108.768, 'duration': 3.702}, {'end': 1122.833, 'text': 'So, based on this definition we can look at a learning system as a box.', 'start': 1114.37, 'duration': 8.463}], 'summary': 'Improving agent skills and efficiency to define performance measures in a learning system.', 'duration': 24.428, 'max_score': 1098.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1098405.jpg'}, {'end': 1260.468, 'src': 'heatmap', 'start': 1182.372, 'weight': 0, 'content': [{'end': 1190.182, 'text': 'So, this is the schematic diagram of a machine learning system or a learner system.', 'start': 1182.372, 'duration': 7.81}, {'end': 1200.875, 'text': 'Inside there are two components, two main components the learner L and the reasoner.', 'start': 1191.003, 'duration': 9.872}, {'end': 1218.966, 'text': 'See the learner takes the experience and from that it can also take the background knowledge and from this the learner builds models.', 'start': 1206.849, 'duration': 12.117}, {'end': 1230.497, 'text': 'and this models can be used by the reasoner which given a task finds the solution to the task.', 'start': 1222.111, 'duration': 8.386}, {'end': 1239.324, 'text': 'So, the learner takes experience and background knowledge and learns a model and the reasoner works with the model.', 'start': 1231.478, 'duration': 7.846}, {'end': 1249.833, 'text': 'and given a new problem or task, it can come up with the solution to the task and the performance measure corresponding to this.', 'start': 1240.698, 'duration': 9.135}, {'end': 1255.022, 'text': 'Now, we will like to look at some examples of machine learning system.', 'start': 1250.855, 'duration': 4.167}, {'end': 1260.468, 'text': 'So, machine learning, there are many domains and applications of machine learning.', 'start': 1256.465, 'duration': 4.003}], 'summary': 'Machine learning system consists of learner and reasoner, which work together to learn models and find solutions for tasks.', 'duration': 48.125, 'max_score': 1182.372, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1182372.jpg'}, {'end': 1366.334, 'src': 'heatmap', 'start': 1293.055, 'weight': 2, 'content': [{'end': 1305.106, 'text': 'Another domain is computer vision, where given an image you want to find out what objects appear in an image and where the objects appear in an image.', 'start': 1293.055, 'duration': 12.051}, {'end': 1319.036, 'text': 'A third domain, robot control, one can use machine learning to design autonomous mobile robots that learn to navigate from their own experience.', 'start': 1306.05, 'duration': 12.986}, {'end': 1330.441, 'text': 'Then in the domain of natural language processing, one can detect where entities are mentioned in natural language.', 'start': 1322.237, 'duration': 8.204}, {'end': 1336.076, 'text': 'and detect what facts are expressed in natural language.', 'start': 1332.014, 'duration': 4.062}, {'end': 1346.76, 'text': 'One can look at a product or movie review and find out if it is positive, negative or neutral that is the sentiment on the review.', 'start': 1336.936, 'duration': 9.824}, {'end': 1353.603, 'text': 'Other applications in NLP include speech recognition, machine translation, etcetera.', 'start': 1347.561, 'duration': 6.042}, {'end': 1360.146, 'text': 'In the financial domain, one can try to predict if a stock will rise or fall.', 'start': 1354.383, 'duration': 5.763}, {'end': 1366.334, 'text': 'one can predict if a user will click on an advertisement or not.', 'start': 1362.04, 'duration': 4.294}], 'summary': 'Machine learning is applied in various domains such as computer vision, natural language processing, and finance to perform tasks like object detection, sentiment analysis, and stock prediction.', 'duration': 109.869, 'max_score': 1293.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1293055.jpg'}, {'end': 1422.251, 'src': 'embed', 'start': 1395.267, 'weight': 7, 'content': [{'end': 1405.316, 'text': 'Then there are other applications such as fraud detection, credit card fraud detection, understand consumer sentiment forecast,', 'start': 1395.267, 'duration': 10.049}, {'end': 1411.001, 'text': "women's conviction rates based on external macroeconomic factors, etcetera.", 'start': 1405.316, 'duration': 5.685}, {'end': 1416.085, 'text': 'So, these are some of the many applications of machine learning.', 'start': 1412.182, 'duration': 3.903}, {'end': 1422.251, 'text': 'Machine learning is a part of many products and systems that we routinely use.', 'start': 1416.466, 'duration': 5.785}], 'summary': 'Machine learning has diverse applications, including fraud detection and consumer sentiment forecast.', 'duration': 26.984, 'max_score': 1395.267, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1395267.jpg'}], 'start': 1098.405, 'title': 'Machine learning systems and applications', 'summary': 'Covers the components and functioning of a machine learning system, emphasizing the importance of defining performance measures. it also explores diverse applications of machine learning, such as medical diagnosis, computer vision, and financial predictions, highlighting its impact across industries.', 'chapters': [{'end': 1255.022, 'start': 1098.405, 'title': 'Understanding machine learning systems', 'summary': 'Discusses the components of a machine learning system, including the learner and reasoner, the process of feeding experience and data to the system, and the importance of defining performance measures, providing an overview of how a machine learning system functions and its relevance in problem-solving.', 'duration': 156.617, 'highlights': ['The learner takes experience and background knowledge to build models, which the reasoner uses to find solutions to tasks.', 'Defining the performance measure is crucial in evaluating the effectiveness of a machine learning system.', 'A machine learning system functions as a box that processes new skills for the agent, improves problem-solving efficiency, and provides solutions to tasks.', 'The schematic diagram of a machine learning system consists of two main components: the learner and the reasoner.']}, {'end': 1422.251, 'start': 1256.465, 'title': 'Applications of machine learning', 'summary': 'Discusses various applications of machine learning, including medical diagnosis, computer vision, robot control, natural language processing, financial predictions, and business intelligence, showcasing its wide-ranging impact across industries.', 'duration': 165.786, 'highlights': ['Machine learning applications span across various domains, such as medicine, computer vision, robot control, natural language processing, financial predictions, and business intelligence, demonstrating its versatile impact.', 'In medicine, machine learning can be used to diagnose diseases by analyzing symptoms, lab measurements, test results, and DNA tests to predict possible diseases or determine none, leveraging historical medical records for treatment optimization.', 'Machine learning can be utilized in computer vision to recognize objects in images and their locations, while in robot control, it aids in designing autonomous mobile robots that learn to navigate through their own experience.', 'Natural language processing applications include entity detection, sentiment analysis in product or movie reviews, speech recognition, and machine translation, showcasing the diverse linguistic capabilities offered by machine learning.', 'The financial domain benefits from machine learning for predicting stock movements, user behavior like clicking on advertisements, and various business intelligence tasks such as forecasting sales quantities, identifying cross-selling opportunities, and understanding consumer sentiment and behavior.']}], 'duration': 323.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1098405.jpg', 'highlights': ['Machine learning system comprises a learner and a reasoner.', 'Defining performance measure is crucial for evaluating machine learning effectiveness.', 'Machine learning has diverse applications in medicine, computer vision, robot control, natural language processing, financial predictions, and business intelligence.', 'In medicine, machine learning aids in disease diagnosis and treatment optimization using historical medical records.', 'Machine learning in computer vision recognizes objects and their locations in images.', 'Robot control benefits from machine learning in designing autonomous mobile robots.', 'Natural language processing applications include entity detection, sentiment analysis, speech recognition, and machine translation.', 'Machine learning is used in the financial domain for predicting stock movements, user behavior, and business intelligence tasks.']}, {'end': 1719.973, 'segs': [{'end': 1475.588, 'src': 'heatmap', 'start': 1423.737, 'weight': 0, 'content': [{'end': 1437.648, 'text': 'If you look at the box that we drew for machine learning systems and discuss how we can go about creating a learner, these are the following steps.', 'start': 1423.737, 'duration': 13.911}, {'end': 1444.814, 'text': 'First of all, we choose the training experience or the training data.', 'start': 1438.249, 'duration': 6.565}, {'end': 1458.317, 'text': 'then we choose the target function or how we want to represent the model.', 'start': 1451.673, 'duration': 6.644}, {'end': 1475.588, 'text': 'So, this is what we want to learn the target function that is to be learned.', 'start': 1470.044, 'duration': 5.544}], 'summary': 'Steps for creating a learner: choose training data, select target function, and learn the function.', 'duration': 51.851, 'max_score': 1423.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1423737.jpg'}, {'end': 1591.785, 'src': 'embed', 'start': 1557.803, 'weight': 1, 'content': [{'end': 1569.789, 'text': 'So the learning algorithm will explore the possible function parameters so that, based on the training experience,', 'start': 1557.803, 'duration': 11.986}, {'end': 1574.675, 'text': 'it can come up with the best function, given its computational limitations.', 'start': 1569.789, 'duration': 4.886}, {'end': 1585.338, 'text': 'So, what is very important? in the designing of a learning algorithm is how to represent the target function.', 'start': 1576.277, 'duration': 9.061}, {'end': 1591.785, 'text': 'Before that what is important is how to represent the training experience.', 'start': 1585.798, 'duration': 5.987}], 'summary': 'Learning algorithm explores function parameters to find best function given computational limitations.', 'duration': 33.982, 'max_score': 1557.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1557803.jpg'}, {'end': 1657.255, 'src': 'embed', 'start': 1618.362, 'weight': 2, 'content': [{'end': 1624.465, 'text': 'And when we are trying to find this class of the functions, we have to make very important decision.', 'start': 1618.362, 'duration': 6.103}, {'end': 1632.188, 'text': 'We can go for a very powerful function class which is very complex, can represent complex concepts.', 'start': 1624.945, 'duration': 7.243}, {'end': 1641.193, 'text': 'But if we choose a powerful or rich representation, if we choose a rich representation of the class of functions.', 'start': 1632.869, 'duration': 8.324}, {'end': 1657.255, 'text': 'then we can represent complex function, but and it will be more useful for subsequent problem solving, but it may be more difficult to learn.', 'start': 1642.924, 'duration': 14.331}], 'summary': 'Choosing powerful function class can represent complex concepts, but may be difficult to learn.', 'duration': 38.893, 'max_score': 1618.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1618362.jpg'}], 'start': 1423.737, 'title': 'Machine learning systems', 'summary': 'Covers steps for creating a machine learning system, such as choosing training data, defining target functions, and specifying function classes. it also discusses designing learning algorithms, choosing function representation, and the trade-off between powerful function classes and defining hypothesis language.', 'chapters': [{'end': 1499.196, 'start': 1423.737, 'title': 'Creating machine learning systems', 'summary': 'Discusses the steps involved in creating a learner for machine learning systems, including choosing training data, defining the target function, and specifying the class of function to be used, with an example of a checkers game.', 'duration': 75.459, 'highlights': ['Choosing the training data is the first step in creating a learner for machine learning systems, followed by defining the target function and specifying the class of function to be used.', 'In the context of creating a machine learning system for playing checkers, the target function would involve determining the move to take based on the given board position.']}, {'end': 1617.301, 'start': 1499.196, 'title': 'Designing learning algorithm', 'summary': 'Discusses the process of designing a learning algorithm, including the selection of function representation, exploration of function parameters, and the importance of representing training experience in terms of domain features.', 'duration': 118.105, 'highlights': ['The learning algorithm explores possible function parameters to come up with the best function based on training experience, considering computational limitations.', 'The training experience can be expressed in terms of features of the domain, requiring a decision on how to represent the target function.', 'The function design involves choosing how to represent the target function and deciding on the appropriate class of functions based on the features.']}, {'end': 1719.973, 'start': 1618.362, 'title': 'Choosing function representation', 'summary': 'Discusses the trade-off between choosing a powerful function class which can represent complex concepts but is difficult to learn, and the importance of features and attributes in defining the hypothesis language.', 'duration': 101.611, 'highlights': ['Choosing a rich representation of the class of functions allows for solving complex problems but may be more difficult to learn.', 'Richer representations are able to represent many types of classes, including complex classes, but are more difficult to learn.', 'The components of representation include the features or attributes of the domain, and the class of functions, also known as the hypothesis language.']}], 'duration': 296.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/T3PsRW6wZSY/pics/T3PsRW6wZSY1423737.jpg', 'highlights': ['Choosing the training data is the first step in creating a learner for machine learning systems, followed by defining the target function and specifying the class of function to be used.', 'The learning algorithm explores possible function parameters to come up with the best function based on training experience, considering computational limitations.', 'Choosing a rich representation of the class of functions allows for solving complex problems but may be more difficult to learn.']}], 'highlights': ['The rise of neural networks in 2005 marked a new era in machine learning, paving the way for deep learning with many deep layers.', 'Support Vector Machines proposed by Vapnik and Cortes in 1995 had strong theoretical standing and empirical results, marking a significant machine learning breakthrough.', 'The kernelized version of SVM, proposed near 2000s, was able to exploit the knowledge of convex optimization, generalization, and kernels, further advancing the capabilities of support vector machines.', 'The course is over 8 weeks and will have 8 modules, covering various topics such as linear regression, decision trees, support vector machines, and neural networks.', 'The first module will include an introduction to machine learning, with subsequent modules focusing on different types of learning, hypothesis space, inductive bias, evaluation, training, test sets, and cross validation.', 'Arthur Samuel developed the first machine learning system at IBM for playing checkers in 1952, enabling the program to learn and improve its gameplay over time.', "Rosenblatt introduced the perceptron in 1957, a significant discovery in the field of neural networks, but Minsky's work in 1969 revealed the limitations of perceptron, leading to a dormant period in neural network research until the 1980s.", 'Widrow and Hoff developed the delta learning rule in the 1950s, which served as a training procedure for perceptrons and contributed to the creation of a good linear classifier.', "Theoretical framework of machine learning was presented, including Valiant's PAC learning theory.", 'Back propagation algorithm was developed, becoming the key ingredient of modern neural network architectures.', 'The intuition of multi-layer perceptron was suggested by Verbos in 1981, contributing to the resurgence of neural network research.', 'Neural network researchers presented the idea of multilayer perceptron (MLP) with practical back propagation (BP) training, leading to an acceleration in 1985-86.', "J. R Quinlan's development of the ID3 algorithm in 1986, known for its simplistic rules, contrasting the black box nature of neural networks, and gaining significant popularity in machine learning.", 'The subsequent development of alternative or improved versions of ID3, such as CART regression trees, which remain popular in machine learning.', 'The increasing prominence of symbolic natural language processing during the 1980s, marking a significant period in the evolution of decision tree learning and machine learning in general.', 'The formal definition of machine learning by Tom Mitchell emphasizes the concept of learning from experience, where computer systems automatically improve with experience, and the models built can be used for prediction, decision making, and solving tasks.', 'Machine learning system comprises a learner and a reasoner.', 'Defining performance measure is crucial for evaluating machine learning effectiveness.', 'Machine learning has diverse applications in medicine, computer vision, robot control, natural language processing, financial predictions, and business intelligence.', 'In medicine, machine learning aids in disease diagnosis and treatment optimization using historical medical records.', 'Machine learning in computer vision recognizes objects and their locations in images.', 'Robot control benefits from machine learning in designing autonomous mobile robots.', 'Natural language processing applications include entity detection, sentiment analysis, speech recognition, and machine translation.', 'Machine learning is used in the financial domain for predicting stock movements, user behavior, and business intelligence tasks.', 'Choosing the training data is the first step in creating a learner for machine learning systems, followed by defining the target function and specifying the class of function to be used.', 'The learning algorithm explores possible function parameters to come up with the best function based on training experience, considering computational limitations.', 'Choosing a rich representation of the class of functions allows for solving complex problems but may be more difficult to learn.']}