CSE474/574: Introduction to Machine Learning
Spring 2016
Instructor: Varun Chandola
Syllabus
A
1
2
3
4
5
B
1
2
3
C
1
2
D
1
2
3
E
1
2
3
Topics
Machine Learning Basics and
Theory
Introduction
Concept Learning
Online Learning and Mistake Bounds
Perceptrons
Neural Networks
Probabilistic Methods
Probability Theory Review
Generative Models
Bayesian Learning
March 11
Spring Break
Linear Models
Linear regression models
Logistic regression
Kernel Methods
Introduction
Maximum Margin Classifiers
Support Vector Machines
Latent Models
Clustering
Mixture Models
Latent Linear Models
Course Review
May 11
Weeks
Reference
W1
W1
W2
W2
W3
Ch1,
Ch2,
Ch7,
Ch4,
Ch4,
W5
W5, W6
W6, W7
Mid Term Exam
W8
Ch2, MURPHY
Ch3/Ch4, MURPHY
Ch5, MURPHY
(8.00 AM - 10.00 AM, Norton 112)
W9,W10
W10
Ch7, MURPHY
Ch8, MURPHY
W10,W11
W11
W11,W12
Ch6, BISHOP
Ch7, BISHOP
Ch7, BISHOP
MITCHELL
MITCHELL
MITCHELL
MITCHELL
MITCHELL
W12
Ch11, MURPHY
W13
Ch11, MURPHY
W14
Ch12, MURPHY
W15
Final Exam (8.00 AM - 11.00 AM, Norton 112)
Readings
• MITCHELL: Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
• MURPHY: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
• BISHOP: Chris Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• MACKAY: David Mackay, Information Theory, Inference, and Learning Algorithms, Cambridge Press, 2003.
• HASTIE: Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009.
Grading Scheme
• Grading Scheme
– Short weekly quizzes using Gradiance (12) – 20%
– Programming Assignments (3) – 30%
– Mid-term Exam (in-class, open book/notes) – 20%
– Final Exam (in-class, open book/notes) – 30%
• All components will be individually curved
– Final grade (Tentative):
A
AB+
B
[92.5, 100]
[87.5, 92.5)
[82.5, 87.5)
[77.5, 82.5)
BC+
C
C-
[72.5, 77.5)
[67.5, 72.5)
[62.5, 67.5)
[57.5, 62.5)
Deliverables
Deliverable
Gradiance 0
Gradiance 1
Assignment 1
Gradiance 2
Gradiance 3
Gradiance 4
Gradiance 5
Gradiance 6
Gradiance 7
Assignment 2
Gradiance 8
Gradiance 9
Assignment 3
Gradiance 10
Gradiance 11
Gradiance 12
Release Date
Jan 25
Feb 01
Feb 10
Feb 08
Feb 15
Feb 22
Feb 29
Mar 07
Mar 21
Mar 23
Mar 28
Apr 04
Apr 06
Apr 11
Apr 18
Apr 25
Due Date
Jan 31
Feb 07
Mar 02
Feb 14
Feb 21
Feb 28
Mar 06
Mar 20
Mar 27
Apr 06
Apr 03
Apr 10
Apr 27
Apr 17
Apr 24
May 02
Undergraduate Recitation Schedule - Tentative
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Topic
No recitation
Python introduction I
Python introduction II
Optimization basics
Probability Basics I
Probability Basics II
Mid Term Review
Spring Break
Using Scikit-learn
Decision Trees & Random Forests
Feature Selection
Clustering Methods
Dimensionality Reduction
Final Review
Notes
• Gradiance quizzes
– Will be released every Monday at 10.00 AM EST
– Due next Sunday at 11.59 PM EST
– Gradiance 0 will not be evaluated (warm up)
– Gradiance 7 will be released on March 7 but will be due on March 20
• All assignments are electronically due on Wednesdays by 08.59 AM EST through UBLearns.
• Hard copies of assignment reports will be due in-class on Wednesdays before the end of the class.