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Machine learning is programming computers to optimize a performance criterion using example data or past experience.
There is no need to “learn” to calculate payroll
Learning is used when:
-Human expertise does not exist (navigating on Mars),
-Humans are unable to explain their expertise (speech recognition)
-Solution changes in time (routing on a computer network)
-Solution needs to be adapted to particular cases (user bio-metrics)
What We Talk About When We Talk About“Learning”
=>Learning general models from data of particular examples
=>Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce.
=>Example in retail: Customer transactions to consumer behavior:
=>People who bought “Da Vinci Code” also bought “The Five People You Meet in Heaven”
=>Build a model that is a good and useful approximation to the data.
Definition:= “KDD is the non-trivial process of identifying valid, novel, potentially useful, and
ultimately understandable patterns in data”.
=>Retail: Market basket analysis, Customer relationship management (CRM)
=>Finance: Credit scoring, fraud detection
=>Manufacturing: Optimization, troubleshooting
=>Medicine: Medical diagnosis
=>Telecommunications: Quality of service optimization
=>Bioinformatics: Motifs, alignment
=>Web mining: Search engines
What is Machine Learning?
=>Study of algorithms that
=>improve their performance
=>at some task
Optimize a performance criterion using example data or past experience.
Role of Statistics: Inference from a sample
Role of Computer science: Efficient algorithms to
=>Solve the optimization problem
=>Representing and evaluating the model for inference
Growth of Machine Learning
Machine learning is the preferred approach to
=>Speech recognition, Natural language processing
=>Medical outcomes analysis
This trend is accelerating
=>Improved machine learning algorithms
=>Improved data capture, networking, faster computers
=>Software too complex to write by hand
=>New sensors / IO devices
=>Demand for self-customization to the user, environment
=>It turns out to be difficult to extract knowledge from human experts’ failure of expert systems in the 1980s.
=>Aka Pattern recognition
=>Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hairstyle
=>Character recognition: Different handwriting styles.
=>Speech recognition: Temporal dependency.
=>Use of a dictionary or the syntax of the language.
=>Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech
=>Medical diagnosis: From symptoms to illnesses
=>Web Advertising: Predict if a user clicks on an ad on the Internet.
Supervised Learning: Uses
Example: decision trees tools that create rules
=>Prediction of future cases: Use the rule to predict the output for future inputs
=>Knowledge extraction: The rule is easy to understand
=>Compression: The rule is simpler than the data it explains
=>Outlier detection: Exceptions that are not covered by the rule, e.g., fraud
=>Learning “what normally happens”
=>Clustering: Grouping similar instances
=>Other applications: Summarization, Association Analysis
=>Customer segmentation in CRM
=>Image compression: Color quantification
=>Bioinformatics: Learning motifs
=>Policies: what actions should an agent take in a particular situation
=>Utility estimation: how good is a state (used by policy)
No supervised output but delayed reward
Credit assignment problem (what was responsible for the outcome)
=>Robot in a maze
=>Multiple agents, partial observe-ability, …
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.
Machine learning systems automatically learn programs from data. This is often a very attractive alternative to manually constructing them, and in the last decade, the use of machine learning has spread rapidly throughout computer science and beyond.
Machine learning is used in Web search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, drug design, and many other applications. A recent report from a reputed Institute asserts that machine learning (a.k.a. data mining or predictive analytics) will be the driver of the next big wave of innovation.
Several fine textbooks are available to interested practitioners and researchers. However, much of the “folk knowledge” that is needed to successfully develop machine learning applications are not readily available in them. As a result, many machine learning projects take much longer than necessary or wind up producing less-than-ideal results. Yet much of this folk knowledge is fairly easy to communicate. This is the purpose of this article. Many different types of machine learning exist, but for illustration purposes, I will focus on the most mature and widely used one: classification.
Nevertheless, the issues I will discuss apply across all of machine learning. A classifier is a system that inputs (typically) a vector of discrete and/or continuous feature values and outputs a single discrete value, the class.
For example, a spam filter classifies email messages into “ spam” or“ not spam,” and its input may be a Boolean vector x= (x1, . . . , xj, . . . , xd), where xj= 1 if the jth word in the dictionary appears in the email and xj= 0 otherwise. A learner inputs a training set of examples(xi, Yi), where xi= (xi,1, . . . , xi,d) is an observed input and Yi is the corresponding output and outputs a classifier.
The test of the learner is whether this classifier produces the correct output yt for future examples xt(e.g., whether the spam filter correctly classifies previously unseen emails as spam or not spam) correct output yt for future examples xt (e.g., whether the spam filter correctly classifies previously unseen emails as spam or not spam).
Suppose you have an application that you think machine learning might be good for. The first problem facing you is the bewildering variety of learning algorithms available.
Which one to use? There are literally thousands available, and hundreds more are published each year. The key to not getting lost in this huge space is to realize that it consists of combinations of just three components. The components are:
A classifier must be represented in some formal language that the computer can handle. Conversely, choosing a representation for a learner is tantamount to choosing the set of classifiers that it can possibly learn. This set is called the hypothesis space of the learner. If a classifier is not in the hypothesis space, it cannot be learned. A related question, which we will address in a later section, is how to represent the input, i.e., what features to use.
An evaluation function (also called objective function or scoring function) is needed to distinguish good classifiers from bad ones. The evaluation function used internally by the algorithm may differ from the external one that we want the classifier to optimize, for ease of optimization (see below) and due to the issues discussed in the next section.
Finally, we need a method to search among the classifiers in the language for the highest-scoring one. The choice of optimization technique is key to the efficiency of the learner, and also helps determine the classifier produced if the evaluation function has more than one optimum. It is common for new learners to start out using off-the-shelf optimizers, which are later replaced by custom-designed ones.
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