CS 445/545 Machine Learning Spring, 2013 See syllabus at

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Presentation transcript:

CS 445/545 Machine Learning Spring, 2013 See syllabus at Lecture slides will be posted on this website before each class.

Use of laptops, phones, etc. in class: –Please don’t, unless you are using it to take notes or view class slides.

What is machine learning? Textbook definitions of “machine learning”: –Detecting patterns and regularities with a good and generalizable approximation (“model” or “hypothesis”) –Execution of a computer program to optimize the parameters of the model using training data or past experience.

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Training Examples: Class 1Training Examples: Class 2 Test example: Class = ?

Any observations from these machine learning examples?

Types of machine learning tasks Classification –Output is one of a number of classes (e.g., ‘A’) Regression –Output is a real value (e.g., ‘$35/share”)

Types of Machine Learning Methods Supervised –provide explicit training examples with correct answers e.g. neural networks with back-propagation Unsupervised –no feedback information is provided e.g., unsupervised clustering based on similarity

“Semi-supervised” –some feedback information is provided but it is not detailed e.g., only a fraction of examples are labeled e.g., reinforcement learning: reinforcement single is single-valued assessment of current state

Relation between “artificial intelligence” and “machine learning”?

Key Ingredients for Any Machine Learning Method Features (or “attributes”) Underlying representation for “hypothesis”, “model”, or “target function”: Hypothesis space

Learning method Data: Divide into two or three parts. –Training data Used to train the model –Validation data Used to select model complexity, to determine when to stop training, or to alter training method –Test data Used to evaluate trained model Evaluation method

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Assumption of all ML methods: Inductive learning hypothesis: Any hypothesis that approximates target concept well over sufficiently large set of training examples will also approximate the concept well over other examples outside of the training set. Difference between “induction” and “deduction”?