CS 534: Machine Learning 

Fall 2015  

Class location: Kelley 1003
Class hour: Tue, Thu 12:00-1:20pm


Instructor: Xiaoli Fern

Email:                xfern at eecs.oregonstate.edu 
Office:               Kelly 3073
Office Hours:    Tue 1:30-3pm
Class email list:   cs534-fall15@engr.orst.edu

Teaching assistants:

      

Email
Office hour
Padideh Danaee
danaeep@oregonstate.edu Thursday 3-4:30 (KEC atrium)
Evgenia Chunikhina
chunikhe@onid.oregonstate.edu
Wednesday 3:30-5:00 pm (KEC atrium)

TA office hour location: Kelley Atrium

Course Information (Quick links: Textbook and materials, assignments, lectures)

This course provides a broad introduction to machine learning and data mining. Topics include: supervised learning (discriminative/generative learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); learning theory (bias/variance tradeoffs; VC theory; large margins); ensemble learning (bagging, boosting). If time allows, we will also cover structured prediction problems and algorithms. Lectures will discuss general issues in these topics and well-established algorithms, both from a computational aspect (how to compute the answer) and a statistical aspect (how to ensure that future predictions are accurate).

Learning Objectives of the Course:

1. Be able to formulate machine learning problems corresponding to different applications. 

2. Understand a range of machine learning algorithms along with their strengths and weaknesses. 

3. Understand the basic theory underlying machine learning. 

4. Be able to apply machine learning algorithms to solve problems of moderate complexity.

5. Be able to read current research papers and understand the issues raised by current research.

Assignments:
The assignments in this course will consist of written problem sets and implementation assignments. The written homework problems are designed to help students build analytical skills required to carry out research related to machine learning. Note that the written assignment will not be graded based on correctness. Rather the TA will record the number of problems that were "completed" (either correctly or incorrectly). Completing a problems requires demonstrating a non-trivial attempt at solving the problem. The judgment of whether a problem was "completed" is left to the instructor and TA.  If unclear whether you have done enough work to be considered "completed", please communicate with the cooresponding TA and/or instructor.


Late policy:

Each student has one late allowance (must be submitted with 48 hours of deadline) without penalty. Once the late allowance is used, your submission will be discounted 10% each day up to two days. For late submission from a team of multiple members, the discount will be applied to each individual student independently according to whether his or her allowance is used.

Exam:
There will be one midterm and one final exam. The exam will be close book but allow one page cheat sheet (letter size, single sided).

Grades:

The final grade will be calculated based on the following breakdown: midterm 25%, final 25%, final project 25%, implementation assignments 25%. In addition, the resulting letter grade will be decreased by one if a student fails to complete at least 80% of the written homework problems.


Textbook and materials



 


Assignments and Projects

Written assignments (Note: Homework solutions will be posted on canvas)
You can submit either a paper copy in person or submit through email to the TA who is in charge of the assignment

You must submit your source code with report to TEACH

Final project:



Lecture Schedule

    You can check out CS534 fall 2014 class webpage  for old schedule/notes.
    Students are expected to complete the reading assignment before the class. This will help students better understand the concept covered in class.