CS 6830: Machine Learning
Fall 2016

Time and Location: Mon, Wed, Fri 10:45 – 11:40am, ARC 101
Instructor: Razvan Bunescu
Office: Stocker 341
Office Hours: Mon, Fri 9:30 – 10:00am, or by email appointment
Email: bunescu @ ohio edu

  • Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2007.

  • Recommended Supplementary Texts:
  • Machine Learning by Tom Mitchell. McGraw Hill, 1997
  • Pattern Classification by Richard O. Duda, Peter E. Hart, & David G. Stork. Wiley-Interscience, 2001
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction by T. Hastie, R. Tibshirani, & J. H. Friedman. Springer Verlag, 2009

  • Course description:
    Machine Learning is concerned with the design and analysis of algorithms that enable computers to automatically find patterns in the data. This introductory course will give an overview of the main concepts, techniques and algorithms that are relevant for the theory and practice of machine learning. The course will cover the fundamental topics of classification, regression and clustering, starting with simple learning models such as perceptrons, decision trees and logistic regression, and ending with more advanced models including Support Vector Machines, Conditional Random Fields and Bayesian Networks. The description of the formal properties of the algorithms will be supplemented with motivating applications in a wide range of areas including natural language processing, computer vision, bioinformatics and music analysis.

    The students are expected to be comfortable with programming and to exhibit a basic level of mathematical dexterity. Relevant background material in linear algebra, probability theory and information theory will be made available during the course.

    Lecture notes:
    1. Syllabus & Introduction
    2. Regression with Linear Models
    3. Fisher Linear Discriminant
    4. Perceptrons and Kernels
    5. Support Vector Machines
    6. Nearest Neighbor Methods
    7. Feature Selection
    8. Decision Trees
    9. Naive Bayes
    10. Logistic Regression
    11. Hidden Markov Models
    12. Conditional Random Fields
    13. PCA, Clustering

    Homework Assignments:
    Final Project:
    Other online reading materials:
    Machine learning software: