A CONCEPT LEARNING TASK

 

A CONCEPT LEARNING TASK

•        Consider the example task of learning the target concept "Days on which Tom enjoys his favourite water sport”

Table: Positive and negative training examples for the target concept EnjoySport.

 

ü  The task is to learn to predict the value of EnjoySport for an arbitrary day, based on the values of its other attributes?

ü  What hypothesis representation is provided to the learner?

 

·       Let’s consider a simple representation in which each hypothesis consists of a conjunction of constraints on the instance attributes.

·       Let each hypothesis be a vector of six constraints, specifying the values of the six attributes

·       Sky

·       AirTemp

·       Humidity

·       Wind

·       Water

·       Forecast

•        For each attribute, the hypothesis will either

ü  Indicate by a "?' that any value is acceptable for this attribute,

ü  Specify a single required value (e.g., Warm) for the attribute, or

ü  Indicate by a "Φ" that no value is acceptable

 

•        If some instance x satisfies all the constraints of hypothesis h, then h classifies x as a positive example (h(x) = 1).

•        The hypothesis that PERSON enjoys his favorite sport only on cold days with high Humidity is represented by the expression

(?, Cold, High, ?, ?, ?)

•        The most general hypothesis -that every day is a positive example - is represented by

(?, ?, ?, ?, ?, ?)

•        The most specific possible hypothesis -that no day is a positive example - is represented by

(Φ, Φ, Φ, Φ, Φ, Φ)

 

Notation

·       The set of items over which the concept is defined is called the set of instances, which is denoted by X.

Example: X is the set of all possible days, each represented by the attributes: Sky, AirTemp, Humidity, Wind, Water, and Forecast

·       The concept or function to be learned is called the target concept, which is denoted by c.

·       c can be any Boolean valued function defined over the instances X

c: X→ {O, 1}

•        Example: The target concept corresponds to the value of the attribute EnjoySport

(i.e., c(x) = 1 if EnjoySport = Yes, and c(x) = 0 if EnjoySport = No).

·       Instances for which c(x) = 1 are called positive examples, or members of the target concept.

·       Instances for which c(x) = 0 are called negative examples, or non-members of the target concept.

·       The ordered pair (x, c(x)) to describe the training example consisting of the instance x and its target concept value c(x).

·       D to denote the set of available training examples.

ü  The symbol H to denote the set of all possible hypotheses that the learner may consider regarding the identity of the target concept.

ü  Each hypothesis h in H represents a Boolean valued function defined over X

h: X→{O, 1}

•        The goal of the learner is to find a hypothesis h such that h(x) = c(x) for all x in X.

Concept Learning Task: Notation

Ø  Given:

·       Instances X: Possible days, each described by the attributes

ü  Sky (with possible values Sunny, Cloudy, and Rainy),

ü  AirTemp (with values Warm and Cold),

ü  Humidity (with values Normal and High),

ü  Wind (with values Strong and Weak),

ü  Water (with values Warm and Cool),

ü  Forecast (with values Same and Change).

·       Hypotheses H:

•        Each hypothesis is described by a conjunction of constraints on the attributes Sky, AirTemp, Humidity, Wind, Water, and Forecast.        

•        The constraints may be "?" (any value is acceptable) , “Φ” (no value is acceptable) , or a specific value.

·       Target concept c: EnjoySport : X → {0, l}     

·       Training examples D: Positive and negative examples of the target function

•        Determine:

•        A hypothesis h in H such that h(x) = c(x) for all x in X.

Table: The EnjoySport concept learning task.

CONCEPT LEARNING

 

CONCEPT LEARNING

·       Learning involves acquiring general concepts from specific training examples.

·       Example: People continually learn

·       general concepts or

·       categories such as

ü  "bird,"

ü  "car,"

ü  "situations in which I should study more in order to pass the exam," etc.

·       Each such concept can be viewed as

·       describing some subset of objects or events

ü  defined over a larger set.

 

·       Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set.

·       Example: A function defined over all animals, whose value is

·       true for birds and

·       false for other animals.

 

Definition: Concept learning - Inferring a Boolean-valued function from training examples of its input and output

What is Concept Learning…?

•        “A Task of acquiring a potential hypothesis (Solution) that best fits the given training examples”.

Types of Machine Learning Systems

 

Types of Machine Learning Systems

There are so many different types of Machine Learning systems that it is useful to classify them in broad categories, based on the following criteria:

•        Whether or not they are trained with human supervision

a.      supervised,

b.     unsupervised,

c.      semi supervised, and

d.     Reinforcement Learning

•        Whether or not they can learn incrementally on the fly

a.      online learning

b.     batch learning

•        Whether they work by simply comparing new data points to known data points, or instead by detecting patterns in the training data and building a predictive model, much like scientists do

a.      instance-based

b.     model-based learning

These criteria are not exclusive; you can combine them in any way you like.

·       For example, a state-of-the-art spam filter may learn on the fly using a deep neural network model trained using examples of spam and ham;

·       this makes it an online, model based, supervised learning system.

Types of Machine Learning Systems

1. Supervised/Unsupervised Learning / Reinforcement Learning
2. Batch and Online Learning
3. Instance-Based Versus Model-Based Learning

YouTube Video Link:  https://www.youtube.com/watch?v=g75YU33NLsA

Source: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

Examples of Machine Learning Applications

Examples of Machine Learning Applications

Let’s look at some concrete examples of Machine Learning tasks, along with the techniques that can tackle them:

       i.          Analyzing images of products on a production line to automatically classify them

     ii.          Detecting tumors in brain scans

   iii.          Automatically classifying news articles

   iv.          Automatically flagging offensive comments on discussion forums

     v.          Summarizing long documents automatically

   vi.          Creating a chatbot or a personal assistant

  vii.          Forecasting your company’s revenue next year, based on many performances’ metrics

viii.          Making your app react to voice commands

   ix.          Detecting credit card fraud

     x.          Segmenting clients based on their purchases so that you can design a different marketing strategy for each segment

   xi.          Representing a complex, high-dimensional dataset in a clear and insightful diagram

  xii.          Recommending a product that a client may be interested in, based on past purchases

xiii.          Building an intelligent bot for a game

 

 

       i.          Analyzing images of products on a production line to automatically classify them:

·       This is image classification, typically performed using convolutional neural networks.

     ii.          Detecting tumors in brain scans:

·       This is semantic segmentation, where each pixel in the image is classified (as we want to determine the exact location and shape of tumors), typically using CNNs as well.

   iii.          Automatically classifying news articles:

·       This is natural language processing (NLP), and more specifically text classification, which can be tackled using recurrent neural networks (RNNs), CNNs, or Transformers.

   iv.          Automatically flagging offensive comments on discussion forums:

·       This is also text classification, using the same NLP tools.

     v.          Summarizing long documents automatically:

·       This is a branch of NLP called text summarization, again using the same tools.

   vi.          Creating a chatbot or a personal assistant:

·       This involves many NLP components, including natural language understanding (NLU) and question-answering modules.

  vii.          Forecasting your company’s revenue next year, based on many performances’ metrics:

·       This is a regression task (i.e., predicting values) that may be tackled using any regression model, such as a Linear Regression or Polynomial Regression model, a regression SVM, a regression Random Forest, or an artificial neural network.

·       If you want to take into account sequences of past performance metrics, you may want to use RNNs, CNNs, or Transformers.

viii.          Making your app react to voice commands:

·       This is speech recognition, which requires processing audio samples: since they are long and complex sequences, they are typically processed using RNNs, CNNs, or Transformers.

   ix.          Detecting credit card fraud:

·       This is anomaly detection.

     x.          Segmenting clients based on their purchases so that you can design a different marketing strategy for each segment:

·       This is clustering.

   xi.          Representing a complex, high-dimensional dataset in a clear and insightful diagram:

·       This is data visualization, often involving dimensionality reduction techniques.

  xii.          Recommending a product that a client may be interested in, based on past purchases:

·       This is a recommender system.

·       One approach is to feed past purchases (and other information about the client) to an artificial neural network, and get it to output the most likely next purchase.

·       This neural net would typically be trained on past sequences of purchases across all clients.

xiii.          Building an intelligent bot for a game: 

·       This is often tackled using Reinforcement Learning, which is a branch of Machine Learning that trains agents (such as bots) to pick the actions that will maximize their rewards over time (e.g., a bot may get a reward every time the player loses some life points), within a given environment (such as the game).

·       The famous AlphaGo program that beat the world champion at the game of Go was built using RL.

YouTube Link: https://www.youtube.com/watch?v=KQyRT7mK4eI

Source: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron