CS 4900/5900: Machine Learning
Fall 2019

Time and Location: Tue, Thu 1:30 – 2:50pm, ARC 321
Instructor: Razvan Bunescu
Office: Stocker 341
Office Hours: Tue, Thu 3:00 – 4:00pm, or by email appointment
Email: bunescu @ ohio edu

Textbook: There is no required textbook for this class. Slides and supplementary materials will be made available on the course website.

Supplementary Texts:
  • Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. MIT Press, 2018
  • Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach, Cambridge University Press, 2012
  • A Course in Machine Learning by Hal Daume III
  • Machine Learning by Tom Mitchell. McGraw Hill, 1997
  • Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2007.
  • 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:
    This course will give an overview of the main concepts, techniques, and algorithms underlying the theory and practice of machine learning. The course will cover the fundamental topics of classification, regression and clustering, and a number of corresponding learning models such as perceptrons, logistic regression, linear regression, Naive Bayes, nearest neighbors, and Support Vector Machines. 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 familiar with basic concepts in linear algebra and statistics. Relevant background material in linear algebra, probability theory and information theory will be made available during the course.

    Lecture notes1:
    1. Syllabus & Introduction
    2. Linear Regression and L2 Regularization
    3. Linear algebra and optimization in Python
    4. Gradient Descent Algorithms
    5. Logistic Regression, Maximum Likelihood, Maximum Entropy
    6. Fisher Linear Discriminant
    7. Perceptrons and Kernels
    8. Support Vector Machines
    9. Nearest Neighbor Methods
    10. Naive Bayes
    11. Clustering
    12. Decision Trees
    13. Reinforcement Learning
    1 The RL lecture slides were selected from the set of slides accompanying the RL textbook by Sutton and Barto, and presented by Quintin Fettes.

    Homework assignments2: 2 The theory questions from assignment 9 were proposed by Quintin Fettes. The spam classification portion of assignment 6 is partly based on a homework assignment developed by Andrew Ng.

    Other online reading materials:
    Machine learning software: