Syllabus Overview:

1/4

1/6

1/8

1/11
 Decision Trees (Part2)
 Homework1 is available. [pdf][tex]

1/13

1/14
 Probability Recitation (at quiz section times/locations)

1/15

1/18
 MLK

1/20

1/21
 Linear Algebra Recitation (at quiz section times/locations)

1/22
1/25
 Naive Bayes
 Homework1 is due before the class [Use Dropbox to submit your homework]
 Homework2 is available [.pdf][.tex][images]

1/27

1/29

2/1
2/3

2/5

2/8

2/10
2/12

2/15
 President's Day
 Homework2 is due before the class [Use Dropbox to submit your homework]
 Homework 3 is available [.pdf][.tex][validation data][test data]
2/17
2/19
2/22

2/24

2/26

2/29
 Boosting
 Homework3 is due before the class. [Use Dropbox to submit your homework]
 Homework 4 is available. [.pdf][.tex][country data]

3/2

3/4

3/7

3/9

3/11
 Neural Networks
 Homework4 is due 11:59PM. [Use Dropbox to submit your homework]
 Machine Learning: a Probabilistic Perspective, Kevin Murphy, MIT Press, 2013.
 Optional: Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007.
 Optional: Machine Learning, Tom Mitchell, McGrawHill, 1997.
 Optional: The Elements of Statistical Learning, Friedman, Tibshirani, Hastie, Springer, 2001.
 Assignment 1: Decision Trees
 Assignment 2: Classifiers: Naive Bayes, Perceptron, Logistic Regression
 Assignment 3: SVMs and Ensembles
 Assignment 4: kMeans and EM.
 Assignments will be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
 As we sometimes reuse problem set questions from previous years, please do not to copy, refer to, or look at any solution keys while preparing your answers. Doing so will be regarded as cheating. We expect you to want to learn and not google for answers.
 Each student has three penaltyfree late day for the whole quarter. Beyond that, late submissions are penalized (10% of the maximum grade per day)
 Comments can be sent to the instructor or TA using this anonymous feedback form . We take all feedback very seriously and will do whatever we can to address any concerns.
Text Books:
Homeworks:
We will have 4 homework assignments, which will be listed below as they are assigned. The assignments will be given out roughly in weeks 2, 4, 6, and 8, and you will have two weeks to complete each one.
Please upload your assignment in a compressed file including codes, executables, writing assignments, and any extra data. Note that there is a deadline for each assignment. Anything uploaded after the deadline will be marked late.
Please be careful to not overwrite an in time assignment with a late assignment when uploading near the deadline.
Each student has three penaltyfree late day for the whole quarter, other than that any late submission will be penalized for each day it is late.
Please let the TA know if you cannot access any of the pages.
Exam:
There will be a final exam for this course (Time and location TBA). The exam is open, you are welcome to bring the book, the lecture slides, and any handwritten notes you have.
Grading:
The final grade will consist of homeworks (70%), a final exam (25%), and course participation (5%).