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Machine Learning. Usman Roshan Dept. of Computer Science NJIT. What is Machine Learning?. “ Machine learning is programming computers to optimize a performance criterion using example data or past experience. ” Intro to Machine Learning, Alpaydin, 2010 Examples: Facial recognition

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machine learning

Machine Learning


Dept. of Computer Science


what is machine learning
What is Machine Learning?
  • “Machine learning is programming computers to optimize a performance criterion using example data or past experience.” Intro to Machine Learning, Alpaydin, 2010
  • Examples:
    • Facial recognition
    • Digit recognition
    • Molecular classification
a little history
A little history
  • 1946: First computer called ENIAC to perform numerical computations
  • 1950: Alan Turing proposes the Turing test. Can machines think?
  • 1952: First game playing program for checkers by Arthur Samuel at IBM. Knowledge based systems such as ELIZA and MYCIN.
  • 1957: Perceptron developed by Frank Roseblatt. Can be combined to form a neural network.
  • Early 1990’s: Statistical learning theory. Emphasize learning from data instead of rule-based inference.
  • Current status: Used widely in industry, combination of various approaches but data-driven is prevalent.
example up close
Example up-close
  • Problem: Recognize images representing digits 0 through 9
  • Input: High dimensional vectors representing images
  • Output: 0 through 9 indicating the digit the image represents
  • Learning: Build a model from “training data”
  • Predict “test data” with model
data model
Data model
  • We assume that the data is represented by a set of vectors each of fixed dimensionality.
  • Vector: a set of ordered numbers
  • We may refer to each vector as a datapointand each dimension as a feature
  • Example:
    • A bank wishes to classify humans as risky or safe for loan
    • Each human is a datapoint and represented by a vector
    • Features may be age, income, mortage/rent, education, family, current loans, and so on
machine learning resources
Machine learning resources
  • Data
    • NIPS 2003 feature selection contest
    • UCI machine learning repository
  • Contests
    • Kaggle
  • Software
    • Python sci-kit
    • R
    • Your own code
  • Not required but highly recommended for beginners
  • Introduction to Machine Learning by Ethem Alpaydin (2nd edition, 2010, MIT Press). Written by computer scientist and material is accessible with basic probability and linear algebra background
  • Applied predictive modeling by Kuhn and Johnson (2013, Springer). More recent book focuses on practical modeling.
some practical techniques
Some practical techniques
  • Combination of various methods
  • Parameter tuning
    • Error trade-off vs model complexity
  • Data pre-processing
    • Normalization
    • Standardization
  • Feature selection
    • Discarding noisy features
  • Basic linear algebra and probability
    • Vectors
    • Dot products
    • Eigenvector and eigenvalue
  • See Appendix of textbook for probability background
    • Mean
    • Variance
    • Gaussian/Normal distribution
  • Implementation of basic classification algorithms with Perl and Python
    • Nearest Means
    • Naïve Bayes
    • K nearest neighbor
    • Cross validation scripts
  • Experiment with various algorithms on assigned datasets
  • Some ideas:
    • Experiment with Kaggle and NIPS 2003 feature selection datasets
    • Experimental performance study of various machine learning techniques on a given dataset. For example comparison of feature selection methods with a fixed classifier.
  • One exam in the mid semester
  • Final exam
  • What to expect on the exams:
    • Basic conceptual understanding of machine learning techniques
    • Be able to apply techniques to simple datasets
    • Basic runtime and memory requirements
    • Simple modifications
grade breakdown
Grade breakdown
  • Assignments and project worth 50%
  • Exams worth 50%