Podcast
Questions and Answers
According to the definition by Tom Mitchell, what are the three components that constitute a well-defined learning task?
According to the definition by Tom Mitchell, what are the three components that constitute a well-defined learning task?
- Performance, Task, Experience (correct)
- Input, Process, Output
- Data, Information, Knowledge
- Algorithms, Models, System
In traditional programming, what are the inputs and output, compared to machine learning?
In traditional programming, what are the inputs and output, compared to machine learning?
- Traditional programming focuses on creating static programs, while machine learning creates adaptive programs.
- Traditional programming uses data and program as inputs and generates output, while machine learning uses data and output as inputs and produces a program. (correct)
- Traditional programming and machine learning both use data and algorithms as input but differ in their optimization techniques.
- Traditional programming uses data as input and generates predictions as output, while machine learning uses algorithms as input and produces models as output.
In which of the following scenarios would machine learning be most appropriate?
In which of the following scenarios would machine learning be most appropriate?
- Developing a database management system for storing customer information.
- Creating a simple calculator application for basic arithmetic operations.
- Navigating a rover on Mars where human expertise is limited due to the environment's novelty. (correct)
- Calculating employee payroll where the rules and parameters are clearly defined and static.
Which of the following tasks is best solved using a machine learning algorithm due to the complexity and variability of the problem?
Which of the following tasks is best solved using a machine learning algorithm due to the complexity and variability of the problem?
Which of the following is an example of a task involving 'recognizing anomalies' that could be addressed using machine learning?
Which of the following is an example of a task involving 'recognizing anomalies' that could be addressed using machine learning?
Samuel's Checkers-Player is a famous example in the field of machine learning. According to Arthur Samuel, what distinguishes machine learning from traditional programming?
Samuel's Checkers-Player is a famous example in the field of machine learning. According to Arthur Samuel, what distinguishes machine learning from traditional programming?
In the context of defining a learning task (T), what is the primary goal when categorizing email messages as spam or legitimate?
In the context of defining a learning task (T), what is the primary goal when categorizing email messages as spam or legitimate?
An autonomous car is being developed. What constitutes the 'Experience (E)' component in the context of machine learning for this car?
An autonomous car is being developed. What constitutes the 'Experience (E)' component in the context of machine learning for this car?
An autonomous car is being developed. What constitutes the 'Performance(P)' component in the context of machine learning for this car?
An autonomous car is being developed. What constitutes the 'Performance(P)' component in the context of machine learning for this car?
How does the application of deep learning in speech recognition impact the overall system performance?
How does the application of deep learning in speech recognition impact the overall system performance?
Consider a checkers-playing program that learns through self-play. Which of the following best describes the 'Experience (E)' component in this scenario?
Consider a checkers-playing program that learns through self-play. Which of the following best describes the 'Experience (E)' component in this scenario?
What is a key characteristic of machine learning that distinguishes it from traditional programming approaches?
What is a key characteristic of machine learning that distinguishes it from traditional programming approaches?
An engineer is designing a machine learning system to classify images of cats and dogs. Which of the following components represents the 'Task (T)' in this scenario?
An engineer is designing a machine learning system to classify images of cats and dogs. Which of the following components represents the 'Task (T)' in this scenario?
Which of the following describes the role of a 'Transducer' in a typical speech recognition system utilizing Machine Learning?
Which of the following describes the role of a 'Transducer' in a typical speech recognition system utilizing Machine Learning?
How did Nevada influence the development of autonomous car technology in the United States?
How did Nevada influence the development of autonomous car technology in the United States?
What is the primary function of LADAR sensors in autonomous vehicles?
What is the primary function of LADAR sensors in autonomous vehicles?
How do object parts relate to edges in the context of deep belief networks for face images?
How do object parts relate to edges in the context of deep belief networks for face images?
In the context of training a deep learning model on multiple object classes, what is the significance of the 'second layer'?
In the context of training a deep learning model on multiple object classes, what is the significance of the 'second layer'?
What role does 'experience' play in reinforcement learning?
What role does 'experience' play in reinforcement learning?
How does semi-supervised learning differ from supervised and unsupervised learning approaches?
How does semi-supervised learning differ from supervised and unsupervised learning approaches?
If you're building an ML model to recognize handwritten words from images, what would be the task (T), performance metric (P), and experience (E)?
If you're building an ML model to recognize handwritten words from images, what would be the task (T), performance metric (P), and experience (E)?
Which industry is least likely to benefit from Machine Learning?
Which industry is least likely to benefit from Machine Learning?
Which of these applications uses Machine Learning?
Which of these applications uses Machine Learning?
What are some of the sensors used in autonomous cars?
What are some of the sensors used in autonomous cars?
With deep learning, what can computers do?
With deep learning, what can computers do?
Flashcards
Machine Learning Definition
Machine Learning Definition
Machine learning is the study of algorithms that improve their performance (P) at some task (T) with experience (E).
Traditional Programming
Traditional Programming
In traditional programming, data and a program are inputted into a computer to produce an output.
Machine Learning Process
Machine Learning Process
In machine learning, data and the desired output are inputted into a computer to produce a learned program.
When to Use Machine Learning
When to Use Machine Learning
Signup and view all the flashcards
Applications of Machine Learning
Applications of Machine Learning
Signup and view all the flashcards
Machine Learning (Samuel)
Machine Learning (Samuel)
Signup and view all the flashcards
Defining a Learning Task
Defining a Learning Task
Signup and view all the flashcards
ML for Autonomous Driving
ML for Autonomous Driving
Signup and view all the flashcards
ML for Email Classification
ML for Email Classification
Signup and view all the flashcards
Supervised Learning
Supervised Learning
Signup and view all the flashcards
Unsupervised Learning
Unsupervised Learning
Signup and view all the flashcards
Semi-supervised Learning
Semi-supervised Learning
Signup and view all the flashcards
Reinforcement Learning
Reinforcement Learning
Signup and view all the flashcards
ML in Speech Recognition
ML in Speech Recognition
Signup and view all the flashcards
Study Notes
- CIS 419/519 is an introductory course on machine learning taught by instructor Eric Eaton.
- Slides for the course were assembled by Eric Eaton with acknowledgement of the many others who made their course materials freely available online.
- Course material is freely accessible online at www.seas.upenn.edu/~cis519
Machine Learning Definition
- Machine learning (ML) is a process that improves system performance through experience, as described by Herbert Simon.
- Tom Mitchell (1998) defines ML as the study of algorithms that improve performance (P) at a task (T) with experience (E).
- A well-defined learning task is given by the tuple <P, T, E>.
Traditional Programming vs Machine Learning
- Traditional programming involves inputting data and a program into a computer to produce output.
- Machine learning involves inputting data and output into a computer to produce a program.
When to Use Machine Learning
- Machine learning (ML) is useful when human expertise doesn't exist, such as in navigating on Mars.
- ML is applicable when humans can't explain their expertise well, like in speech recognition.
- Models must be customized for personalized applications such as medicine.
- Machine learning is valuable when models rely on massive datasets, such as in genomics.
- Machine learning is not always useful as demonstrated by calculating payroll where there is no need to "learn".
Applications of Machine Learning
- Recognizing patterns in facial identities, expressions, handwritten or spoken words, and medical images.
- Generating patterns such as generating images or motion sequences.
- Recognizing anomalies like unusual credit card transactions or sensor readings in a nuclear power plant.
- Prediction of future stock prices or currency exchange rates.
Sample Applications
- Machine learning is used in web search, computational biology, finance, and e-commerce.
- It is applied in space exploration, robotics, information extraction, social networks, and debugging software.
Samuel's Checkers-Player
- Arthur Samuel (1959) defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
Defining the Learning Task
- The learning task involves improving performance (P) on a task (T) based on experience (E).
- Example: playing checkers, performance is measured by the percentage of games won against an arbitrary opponent, with experience gained through playing practice games against itself.
- Example: the task of recognizing hand-written words, performance is measured by the percentage of words correctly classified, with experience gained from a database of human-labeled images of handwritten words.
- Example: the task of driving on four-lane highways using vision sensors, performance is measured by the average distance traveled before a human-judged error, with experience gained from images and steering commands recorded while observing a human driver.
- Example: categorize email messages as spam or legitimate, performance is measured by the percentage of email messages correctly classified, with experience gained from a database of emails, some with human-given labels.
Autonomous Cars
- Nevada legalized autonomous cars to drive on roads in June 2011.
- As of 2013, Nevada, Florida, California, and Michigan had legalized autonomous cars.
- Autonomous car sensors include obstacle detection LADARS, 360° 3-d LADAR, GPS/INU, and Stereo Cameras.
- Autonomous car technology includes laser terrain mapping and adaptive vision
Deep Learning
- Deep learning is a trending topic featured in headlines for its role in areas like image recognition and speech technology.
- Deep Belief Nets on Face Images uses pixels to recognize edges, combinations of edges to recognize object parts and object parts to form object models.
- Learning of object parts.
- Training on multiple objects such as cars, faces, motorbikes, airplanes uses four classes.
- shared and object-specific features on the second layer.
- More specific features are trained on the third layer..
- Scene labeling can be accomplished via deep learning.
- Deep learned models can infer images from incomplete images.
Machine Learning in Automatic Speech Recognition
- Automatic speech recognition systems typically involve feature extraction, a neural network, a decoder, and a transducer and language model.
- Machine learning is used to predict phone states from the sound spectrogram.
- Deep learning leads the way in speech recognition.
- Zeiler et al. published a study of speech recognition using rectified linear units in ICASSP 2013.
Impact of Deep Learning in Speech Technology
- Deep learning has made an impact on speech technology as seen in Google Now, Dragon Dictation and Siri.
Types of Learning
- Supervised (inductive) learning involves being given training data + desired outputs (labels).
- Unsupervised learning involves being given training data without desired outputs.
- Semi-Supervised learning is is being given training data + a few desired outputs.
- Reinforcement learning involves rewards from a sequence of actions.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
