University of Minnesota
CSci 5521 Sec 002 - Intro to Machine Learning

CSci 5521 sec 002 -- Fall 2018 -- Course Syllabus (preliminary)

Class Hours
Lecture: MW2:30-3:45pm in Tate 105

Introduction to Machine Learning; by Ethem Alpaydin (3rd ed) 201?.

Instructor: Prof. Daniel Boley,
Office: KHKH room 6-209
Phone: 612-625-3887
Office Hours: MW 4-5pm (right after class)
To avoid my e-mail spam filter, please include the string "5521" in the subject line.

TA: Xinyan Li
Office: KHKH 2-209
Office Hours: TTh 1-2pm
TA: Tatiana Lenskaia
Office: KHKH 2-209
Office Hours: M 10-11, F 12-1

Assignment Plan (not in order)
If the number of exams and/or assignments changes, the relative weights will be adjusted.

  • Several classroom exercises and/or pop quizzes: up to 10% of final grade
  • 3 midterms: 50% of final grade
  • 4-5 assignments: 40% of final grade

General Information
Neural networks, non-parametric windowing, and Bayes statistical theory are three popular methods for recognizing and classifying patterns - the process of Pattern Recognition. These are the basic machine learning algorithms applicable to high-dimensional numerical data. We introduce the fundamental concepts of these various approaches, including the classification phase and the learning phase. Part of the class will be devoted to methods for unsupervised learning and classification. We assume just some knowledge of elementary statistics, calculus, and elementary linear algebra at the upper division undergraduate level. A combination of written assignments and programming projects will be used to illustrate the concepts. Most if not all programming will be done in Matlab, which includes extensive on-line introductory material for those unfamiliar with it.

TOPICS by week

  1. Intro: What is Machine Learning (Chap 1)
  2. Supervised Learning: Some basic concepts (Chap 2)
    HW0 due
  3. Bayes Decision Theory: Conditional Probability (Chap 3)
  4. . . .  Discriminant Functions, Normal Dist. (Chap 3)
    HW1 due
  5. Estimating Unknown Probability Densities, (Chap 4) Parametric Classification (Chap 4)
    Midterm 1: October 3.
  6. Multivariate Methods: estimation and classification (Chap 5)
  7. Nonparametric Density Estimation & Classification (Chap 8)
    HW2 due
  8. Dimensionality Reduction: feature selection PCA MDS (Chap 6)
  9. Scatter Matrices - Fishers Linear Discriminant (Chap 5)
    HW3 due
  10. Linear Support Vector Machines (chap 13)
    Midterm 2: November 14.
  11. Unsupervised Clustering: K-means EM (Chap 7)
  12. Multilayer Perceptrons (Chap 11)
    HW4 due
  13. . . .  continued
  14. Kernal SVM (chap 13)
  15. Review
    HW5 due
    Midterm 3: December 12 OR Final Exam 8-10am Saturday December 15

Students will be expected to implement several of the algorithms on a digital computer in MATLAB and they should be familiar with basic programming techniques, as well as being able use the help system in MATLAB. Students should also be acquainted with the basic concepts in linear algebra and probability, though some of this will be reviewed during the course.

All items handed in to be graded must represent the individual effort of whoever's name(s) appears on the item. At a minimum, violators of this policy will fail the course and/or will have their names recorded at the appropriate University or Department office. Mutual discussion of each individual's results in the homeworks is encouraged, as long as the results themselves represent individual efforts. Some assignments will be done by pairs of students; such items should be handed in as a single item listing the names of all participants. To pass the course, you will have to achieve a passing grade on the exams alone, and do satisfactorily on the homeworks. After any graded item is first handed back, you have at most one week to ask about the way it was graded.

You should also be aware of the following University-wide policies:

Late submissions: Homeworks will be accepted up to two working days after the due date with a deduction of 5% of the total grade per day, but before the answers have been posted, which ever occurs first. Classroom exercises handed in outside of class will receive only half credit and will be accepted only the same day by e-mail. Students signing up for the class during the first two weeks of the semester must hand in missed classroom exercises during the day of their first attendance. These deadlines may be extended for excused absences on a case-by-case basis, but only for a limited number of occurences.

Back to class home page.