1. Machine Learning UsmanRoshan Dept. of Computer Science NJIT

2. 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

3. 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.

4. 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

5. 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

6. Machine learning resources • Data • NIPS 2003 feature selection contest • mldata.org • UCI machine learning repository • Contests • Kaggle • Software • Python sci-kit • R • Tensorflow • Your own code

7. Machine Learning techniques and concepts we will learn in this course

8. Textbooks • Not required but highly recommended for beginners • Introduction to Machine Learning by EthemAlpaydin (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 AfshinRostamizadeh, AmeetTalwalkar, and MehryarMohri (2012, MIT Press) • Learning with Kernels by Scholkopf and Smola (2001, MIT Press) • Applied predictive modeling by Kuhn and Johnson (2013, Springer). This book focuses on practical modeling.

9. 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

10. 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

11. Assignments • Implementation of basic classification algorithms with Perl and Python • Nearest Means • Naïve Bayes • Gradient descent for least squares, hinge loss, and logistic loss • Algorithm for decision stump • Bagged decision stumps • K-means clustering • Optional feature learning assignment

12. Project • Feature selection on high dimensional genomic data

13. 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

14. Grade breakdown • Assignments and project worth 50% (40% assignments, 10% project) • Exams worth 50% (25% mid-term, 30% final)