Class location: Kelley 1003
Class hour: Tue, Thu 12:001:20pm
Email:
xfern at
eecs.oregonstate.edu
Office:
Kelly 3073
Office Hours: Tue 1:303pm
Class email list: cs534fall15@engr.orst.edu
Email 
Office hour 

Padideh Danaee 
danaeep@oregonstate.edu  Thursday 34:30 (KEC atrium) 
Evgenia Chunikhina 
chunikhe@onid.oregonstate.edu 
Wednesday 3:305:00 pm (KEC atrium) 
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:
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:
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.
Date 
Topic 
Reading Assignment 
Annoucements 
Week 0 (9/24) 
Course
logistics Intro to ML (pdf slides) Linear Regression (pdf slides) 
The
decipline of Machine Learning Differential calculus Section 1.1 CS229 linear algebra review, Sections 1,2, 3.17, 4 CS229 probability review 
Welcome to cs534!!! 
Week 1 (9/29,10/1) 
Discriminative classification
models: perceptron
(pdf
slides) 
CS229
note1 Part I CIML chapter 3 
Discussion/Review session on
linear algebra Led by Evgenia: notes part 1 and part 2 Time: Wed 67PM Location: COVL221 
Week 2 (10/6,10/8) 
Logistic regression (pdf slides) Generative classification models: LDA (notes, slides) and Naive Bayes (pdf slides) 
CS229
Note1, part II CS229 Note 2, part IV Reading on generative vs. discriminative classifiers 
Discussion/review session on
probablity theory Led by Evgenia: notes part 1 and part 2 Time: Wed 67pm Location: COVL221 
Week 3
(10/13,10/15) 
Support Vector Machines (Part I) Support Vector Machines (Part II) Updated combined slides for SVM 
An
introduction to constrained optimization, Duality
and KKT conditions Convex Optimization part I 
Final project information posted
on blackboard under course document Discussion/review session on optimization Led by Padideh: notes Cvx Optimization 1 Time Wed 67 location: COVL221 
Week 4
(10/20,10/22) 
Decision Trees (pdf slides) Ensemble methods (pdf slides) 
Convex
Optimization part II Reading on DT posted on canvas under files An optional reading on boosting 
Discussion/review session on
optimization Led by Padideh Time: Wed 67pm location: COVL221 
Week 5 (10/27, 10/29) 
Hierarichical clustering, Kmeans
and GMM (pdf
slides) 
Andrew Ng's EM notes 

Week 6 (11/3 11/5) 
Midterm (11/3 in
class) Kmeans, GMM Expectation Maximization (pdf slides) 
Sample
Midterm Solution will be made available on canvas on Oct 31st 

Week 7 (11/10
11/12) 
EM cont. Spectral clustering (slides) Model selection for clustering (slides) Dimension reduction (slides) 
Normalized
cut and image segmentation by Jianbo Shi and
Jitendra Malik, IEEE PAMI 2000 

Week 8 (11/17
11/19) 
Neural Networks (slides) Bias and Variance Decomposition (slides) Computational learning theory part 1 (slides) 
Reading on neural net posted on canvas 

Week 9 (11/24 
11/26) 
Computational learning theory part
2 (slides) 
Thur thanksgiving. No class 

Week 10 (12/1 
12/3) 

Project presentations 

Final Exam Week 
Final exam: Thur 12:001:50 in
regular class room Final exam coverage: From Ensemble methods (inclusive) on 
Sample exam Solution will be made available on canvas on Dec 7th 