CS 480/580L Introduction to Machine Learning     Spring 2012


This course provides a broad introduction to machine learning and its applications. It will introduce students the basic ideas and intuition behind different machine learning techniques as well as a more formal understanding of how and why they work. The course will also discuss recent applications of machine learning, such as to bioinformatics, social computing, and autonomic computing.

This course is designed for CS graduate students and senior undergraduate students.

Class Schedule: T R 2:50 PM 4:15 PM

Classroom:  SW 321

Instructor: Dr. Lei Yu  

TA: Kaoning Hu

Telephone:  (607) 777-6250


Email: lyu AT cs DOT binghamton DOT edu  

Email: khu1 AT binghamton DOT edu

Office Location: G16, Engineering Building

Office Location:

Office Hours: T R 1:00PM - 2:00PM or by appointment

Office Hours:


  • Required courses: CS 333 (Algorithms) and MATH 327 (Probability with Statistical Methods), or equivalents
  • Programming: programming assignments can be implemented in any popular programming languages, such as C, C++, Java, or Matlab. No programming-specific issues will be covered in this course.


Major topics include:

  • Supervised learning (parametric/non-parametric learning, generative/discriminative learning, hypothesis evaluation)
  • Computational learning theory (bias/variance tradeoffs, PAC learning, VC theory)
  • Unsupervised learning (k-Means, EM, hierarchical clustering, clustering evaluation)
  • Reinforcement learning (Q-learning, temporal difference learning)
  • Semi-supervised learning (semi-supervised classification, semi-supervised clustering)
  • Ensemble learning (bagging, boosting)

Textbook (recommended):

  • Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, 2004.
  • Machine Learning, Tom Mitchell, McGraw Hill, 1997.


There will be 5 written/programming assignments during the semester.


Each student will be required to give one individual or group (of two students) presentation on a selected topic (a list of topics given by the instructor).


There will be several quizzes in class. No midterm or final exam for this class.


Final grades will be based on class participation (10%), homework (5 assignments, 50%), quizzes (20%), presentation (20%)

Academic Integrity:

Discussion of general concepts and questions concerning the homework assignments among students is allowed. However, each of you is expected to work on the homework solutions on your own. Sharing of any part of solutions is prohibited. If you are unclear about the policy, please consult with the instructor before you act. Suspected cases of academic misconduct will be pursued fully in accordance to the Student Academic Honesty Code of Thomas J. Watson School of Engineering and Applied Science, Binghamton University.

Late Policy:

Each assignment is due at the beginning of class on the due date. Any assignment received within the next 24 hours will be penalized by 20% of the full credit; any assignment received within the time between 24 hours and 48 hours pass the deadline is penalized by 50% of the full credit; No assignment will be accepted after 48 hours pass the deadline. Rare exceptions of this policy may be made at the discretion of the instructor under demonstrably circumstances.