CS 534  Machine Learning
Course Overview
Machine learning is an exciting field of computer science that impacts many applications both from a consumer standpoint (e.g., Microsoft Kinect, iPhone’s Siri, Netflix recommendations) and the sciences and medicines (e.g., predicting genomeprotein interactions, detecting tumors, personalized medicine). In this course, students will learng the fundamental theory and algorithms of machine learning and how to apply machine learning to solve problems. Prerequisites: A course in linear algebra and previous exposure to statistics and probability theory. Homeworks and project will require programming ability in Python, Matlab, R, or C/C++..
Course Logistics
 Piazza: All annoucements, assignment clarifications, and slide corrections will be posted here. Make sure you check it on a regular basis.
 Office Hours: (Caveat  Office hours may change from time to time, in which case an announcement will be made on Piazza.)
 Joyce Ho: M 1:30 PM  3:30 PM, W 9:30 AM  12:00 PM @ MSC W414
 Rongmei Lin: F 12:00 PM  2:00 PM @MSC N410
 Textbooks
 Required: The Elements of Statistical Learning: Data Mining, Inference, and Prediction), by Trevor Hastie, Robert Tibshirani & Jerome Friedman
 Supplemental: Machine Learning: a Probabilistic Perspective, by Kevin Murphy
 Supplemental: Pattern Recognition and Machine Learning, by Christopher Bishop
Course Schedule
#  Date  Topic  Materials  References  Assignments 

Introduction and Review  
1  1/10  Overview & Course Logistics 
Lecture 1 iPythonIntro.ipynb 


2  1/12  Random Variables & Probability Review 
Lecture 2 Bayesian Review 


3  1/17  Linear Algebra & Optimization Review 
Lecture 3 NumpyBasics.ipynb 


Supervised Learning I  
4  1/19  Statistical Decision Theory & Linear Regression 
Lectures 4  5 PandasBasics.ipynb Teams.csv (data file) LinearRegression.ipynb 


5  1/24  
6  1/26  Linear Classification 
Lectures 67 LDA.ipynb LogisticRegression.ipynb 


7  1/31  
Validation, Model Selection, and Theory  
8  2/2  Learning Theory  Lecture 8 


9  2/7  Validation 
Lecture 9 CrossValidation.ipynb 

10  2/9  Bootstrap & Model Selection  Lectures 1011  
11  2/14  
Supervised Learning II  
12  2/16  Boosting, Trees & Additive Models 
Lectures 1213 DecisionTree.ipynb 

13  2/21  
14  2/23  Ensembles & Random Forests  Lecture 14 


15  2/28  Support Vector Machines 
Lectures 1516 SVM.ipynb 


16  3/2  
Supervised Learning II  
17  3/14  Neural Networks 
Lectures 1718 Neural Network Playground Handwritten Digit Visualization plot_mnist_filters.ipynb by Scikitlearn 


18  3/16  
19  3/21  K Nearest Neighbors  Lecture 19 

Homework 4 
Midterm  
20  3/23  Midterm  
Unsupervised Learning  
21  3/28  Dimensionality Reduction 
Lecture 21 DimensionalityReduction.ipynb 


22  3/30  Clustering & Mixture Models  Lecture 22  
Other Topics  
23  4/4  Topic Models  Lecture 23 


24  4/6  Hidden Markov Models  Lecture 24 


25  4/11  Deep Learning  Lecture 25  
26  4/13  Recommendation Systems  Lecture 26 


Project Presentations  
27  4/18  Presentations  
28  4/20 
Course Grading
Component  Weight 

Homeworks  40% 
Midterm  15% 
Project  40% 
Participation  5% 
Assignment and Exam Policy
Assignments
 Assignments are due electronically at 11:59 PM on Canvas.
 Each student receives six late days that can be used on assignments throughout the semester. These late days extend the deadline for 24 hours, and can be distributed amongst the 45 assignments, but no more than 3 late days may be used on any assignment.
 After the six late days are used, the assignment will be penalized 10% off per day up to 3 late days.
 Late days apply to the entire assignment, so handing in one problem late counts as a late day towards the whole assignment.
 After 3 late days on any given assignment you will receive no credit for the asignment. Exam
 All exams must be taken promptly at the required time.
 Requests for rescheduling the midterm exam will only be considered if the request is made at least a week prior to the exam date.
The above policies will be waived only in an “emergency” situation with appropriate documentation.
Honor Code
All class work is governed by the College Honor Code and Departmental Policy. It is acceptable and encouraged to discuss homeworks with other students. However, this should be noted on your submitted homework and all code and writeup must be written by yourself. Any code and writeup that is found to be similar is grounds for an honor code investigation by the Director of Gradute Studies, Laney Graduate School, and the honor council.