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COMP 875: Introductions,[object Object],[object Object]
Why are you interested in machine learning and how does it relate to your research?
What topics would you like to see covered in this course?,[object Object]
Machine Learning Steps,[object Object],Data collection:Start with training data for which we know the correct outcome provided by a “teacher”,[object Object],Representation: Decide how to encode the input to the learning program,[object Object],Modeling:Choose a hypothesis class– a set of possible explanations for the data,[object Object],Estimation:Find best hypothesis you can in the chosen class,[object Object],Model selection:We may reconsider the class of hypotheses given the outcome,[object Object],Each of these steps can make or break the learning outcome,[object Object],Source: G. Shakhnarovich,[object Object]
Learning and Probability,[object Object],There are many sources of uncertainty with which learning algorithms must cope:,[object Object],Variability of the data,[object Object],Dataset collection,[object Object],Measurement noise,[object Object],Labeling errors,[object Object],Probability and statistics provide an appropriate framework to deal with uncertainty,[object Object],Some basic statistical assumptions:,[object Object],Training data is sampled from the “true” underlying data distribution,[object Object],Future test data will be sampled from the same distribution,[object Object],Source: G. Shakhnarovich,[object Object]
Example of a learning problem,[object Object],Given: training images and their categories,[object Object],What are the categories of these test images?,[object Object],Possible representation: image of size n×n pixels -> vector of length n2 (or 3n2 if color),[object Object],Source: G. Shakhnarovich,[object Object]
The Importance of Representation,[object Object],Dimensionality,[object Object],Beyond vectors: complex or heterogeneous input objects,[object Object],Web pages,[object Object],Program traces,[object Object],Images with captions or metadata,[object Object],Video with sound,[object Object],Proteins,[object Object],Feature extraction and feature selection,[object Object],What measurements/information about the input objects are the most useful for solving the given problem?,[object Object],Successful representation requires domain knowledge!,[object Object],If we could find the “ideal” feature representation, we would not even need learning!,[object Object]
Types of learning problems,[object Object],Supervised,[object Object],Classification,[object Object],Regression,[object Object],Unsupervised,[object Object],Semi-supervised,[object Object],Reinforcement learning,[object Object],Active learning,[object Object],….,[object Object]
Supervised learning,[object Object],Given training examples of inputs and corresponding outputs, produce the “correct” outputs for new inputs,[object Object],Two main scenarios:,[object Object],Classification:outputs are discrete variables (category labels). Learn a decision boundary that separates one class from the other,[object Object],Regression:also known as “curve fitting” or “function approximation.” Learn a continuous input-output mapping from examples (possibly noisy),[object Object]
Regression: example 1,[object Object],Suppose we want to predict gas mileage of a car based on some characteristics: number of cylinders or doors, weight, horsepower, year etc.,[object Object],Source: G. Shakhnarovich,[object Object]
Regression: example 2,[object Object],Training set: faces (represented as vectors of distances between keypoints) together with experimentally obtained attractiveness rankings,[object Object],Learn: function to reproduce attractiveness ranking based on training inputs and outputs,[object Object],Attractiveness score f(v),[object Object],Vector of distances v,[object Object],T. Leyvand, D. Cohen-Or, G. Dror, and D. Lischinski, Data-driven enhancement of facial attractiveness, SIGGRAPH 2008 ,[object Object]
Regression: example 3,[object Object],Input: scalar (attractiveness score),[object Object],Output: vector-valued object (face),[object Object],B. Davis and S. Lazebnik, “Analysis of Human Attractiveness Using Manifold Kernel Regression,” ICIP 2008,[object Object]
Regression: example 4,[object Object],Input: scalar (age),[object Object],Output: vector-valued object (3D brain image),[object Object],B. C. Davis, P. T. Fletcher, E. Bullitt and S. Joshi, "Population Shape Regression From Random Design Data", ICCV, 2007.,[object Object]
Structured Prediction,[object Object],Word,[object Object],Image,[object Object],Source: B. Taskar,[object Object]
Structured Prediction,[object Object],Parse tree,[object Object],Sentence,[object Object],Source: B. Taskar,[object Object]
Structured Prediction,[object Object],Word alignment,[object Object],Sentence in two languages,[object Object],Source: B. Taskar,[object Object]
Structured Prediction,[object Object],Bond structure,[object Object],Amino-acid sequence,[object Object],Source: B. Taskar,[object Object]
Structured Prediction,[object Object],Many image-based inference tasks can loosely be thought of as “structured prediction”,[object Object],Data association problem,[object Object],model,[object Object],Source: D. Ramanan,[object Object]
Other supervised learning scenarios,[object Object],Learning similarity functions from relations between multiple input objects,[object Object],Pairwise constraints,[object Object],Source: X. Sui, K. Grauman,[object Object]
Other supervised learning scenarios,[object Object],Learning similarity functions from relations between multiple input objects,[object Object],Triplet constraints,[object Object],Source: X. Sui, K. Grauman,[object Object]
Unsupervised Learning,[object Object],Given only unlabeled data as input, learn some sort of structure,[object Object],The objective is often more vague or subjective than in supervised learning. This is more of an exploratory/descriptive data analysis,[object Object]
Unsupervised Learning,[object Object],Clustering,[object Object],Discover groups of “similar” data points,[object Object]
Unsupervised Learning,[object Object],Quantization,[object Object],Map a continuous input to a discrete (more compact) output,[object Object],2,[object Object],1,[object Object],3,[object Object]
Unsupervised Learning,[object Object],Dimensionality reduction, manifold learning,[object Object],Discover a lower-dimensional surface on which the data lives,[object Object]
Unsupervised Learning,[object Object],Density estimation,[object Object],Find a function that approximates the probability density of the data (i.e., value of the function is high for “typical” points and low for “atypical” points),[object Object],Can be used for anomaly detection,[object Object]
Other types of learning,[object Object],Semi-supervised learning:lots of data is available, but only small portion is labeled (e.g. since labeling is expensive),[object Object]
Other types of learning,[object Object],Semi-supervised learning:lots of data is available, but only small portion is labeled (e.g. since labeling is expensive),[object Object],Why is learning from labeled and unlabeled data better than learning from labeled data alone?,[object Object],?,[object Object]
Other types of learning,[object Object],Active learning: the learning algorithm can choose its own training examples, or ask a “teacher” for an answer on selected inputs,[object Object],S. Vijayanarasimhan and K. Grauman, “Cost-Sensitive Active Visual Category Learning,” 2009 ,[object Object]
Other types of learning,[object Object],Reinforcement learning: an agent takes inputs from the environment, and takes actions that affect the environment. Occasionally, the agent gets a scalar reward or punishment. The goal is to learn to produce action sequences that maximize the expected reward (e.g. driving a robot without bumping into obstacles),[object Object],Apprenticeship learning: learning from demonstrations when the reward function is initially unknown,[object Object],Autonomous helicopter flight: Pieter Abbeelhttp://heli.stanford.edu/,[object Object]
Generalization,[object Object],The ultimate goal is to do as well as possible on new, unseen data (a test set), but we only have access to labels (“ground truth”) for the training set,[object Object],What makes generalization possible?,[object Object],Inductive bias: set of assumptions a learner uses to predict the target value for previously unseen inputs,[object Object],This is the same as modeling or choosing a target hypothesis class,[object Object],Types of inductive bias,[object Object],Occam’s razor,[object Object],Similarity/continuity bias: similar inputs should have similar outputs,[object Object],…,[object Object]
Achieving good generalization,[object Object],Consideration 1:Bias,[object Object],How well does your model fit the observed data?,[object Object],It may be a good idea to accept some fitting error, because it may be due to noise or other “accidental” characteristics of one particular training set,[object Object],Consideration 2: Variance,[object Object],How robust is the model to the selection of a particular training set?,[object Object],To put it differently, if we learn models on two different training sets, how consistent will the models be?,[object Object]
Bias/variance tradeoff,[object Object],[object Object],[object Object]
  Models with too few parameters may not fit the data well (high bias) but are consistent across different training sets (low variance)2,[object Object]
Bias/variance tradeoff,[object Object],[object Object]
  Generalization error is due to overfitting
  Models with too few parameters may not fit the data well (high bias) but are consistent across different training sets (low variance)

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Introduction

  • 1.
  • 2. Why are you interested in machine learning and how does it relate to your research?
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
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  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. Generalization error is due to overfitting
  • 36. Models with too few parameters may not fit the data well (high bias) but are consistent across different training sets (low variance)
  • 37.
  • 38.
  • 39.
  • 40.