Usman Roshan Dept. of Computer Science NJIT 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 Digit recognition Molecular classification

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 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 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 datapoint and 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 Data NIPS 2003 feature selection contest mldata.org UCI machine learning repository Contests Kaggle Software Python sci-kit R Your own code

Machine Learning techniques and concepts we will learn in this course Bayesian classification: Univariate and multivariate Linear regression Maximum likelihood estimation Naïve-Bayes Perceptron and basic single layer neural networks Linear discrimination and gradient descent optimization: Least squares Logistic regression Support vector machines Kernel methods Regularized risk minimization Bayesian decision theory and error bounds Decision trees, random forests, and boosting Feature selection Dimensionality reduction: PCA Fisher discriminant Maximum margin criterion Clustering Hidden Markov models Big Data methods Representation learning Deep learning

Textbooks 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 Foundations of Machine Learning by Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri (2012 MIT Press) Applied predictive modeling by Kuhn and Johnson (2013, Springer). This book focuses on practical modeling.

Some practical techniques Combination of various methods Randomization methods Parameter tuning Error trade-off vs model complexity Data pre-processing Normalization Standardization Feature selection Discarding noisy features

Background Basic linear algebra and probability Vectors Dot products Eigenvector and eigenvalue See Appendix of textbook for probability background Mean Variance Gaussian/Normal distribution Also see basic and applied stats slides on course website

Assignments Implementation of basic classification algorithms with Perl and Python Nearest Means Naïve Bayes Gradient descent for least squares, hinge loss, and logistic loss CART algorithm for decision tree K-means clustering Optional feature learning assignment

Project Feature selection on high dimensional genomic data

Exams 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 Assignments and project worth 50% Exams worth 50%