matthewdschulman / cis_419_assignment2 Goto Github PK

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# README FILE
# Introduction to Machine Learning
# Assignment 2
#
# After completing the assignment, fill in this README file.  It asks you to duplicate
# your answer to several questions.  Be certain that your answers in this README file
# (which will be used for auto-grading) match the answers in your PDF writeup.
#
# This README file is formatted in YAML to allow it to be machine readable.  Please
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# If you run into any problems that you can't fix easily in listing your sources or 
# providing feedback on the assignment, just include your multi-line string answers to
# these parts as comments.  Everything else (all simple one-word or numeric answers) must
# be properly formatted YAML.
# 
# Assignment Version 20140929a
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# Personal information
FIRSTNAME: Eric
LASTNAME:  Eaton
PENNKEY:   eeaton
PENNID:    123456

# Which course are you enrolled in?  (enter 419 or 519)
COURSE:  419 


# List all sources of help that you consulted while completing this assignment 
# (other students, colleagues, textbooks, websites, etc.).  This includes anyone you 
# briefly discussed the homework with.  If you received help from the following sources, 
# you do not need to cite it: course instructor, course teaching assistants, course 
# lecture notes, course textbooks or other readings.
# 
# If you didn't receive help from anyone, write "none".

SOURCESCONSULTED: |
    While completing the assignment, I consulted the following sources: 
    Chapter 2 of Pattern Recognition and Machine Learning by C. Bishop
    Chapter 5 of Machine Learning, A Probabilistic Perspective by K. Murphy


 
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# Answers to Problem 2:  Fitting an SVM by hand
#
# Please list your final answers below for auto-grading.
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# Problem 2b: what is the value of the margin  (only your final numeric answer)
PROB_SVMBYHAND_MARGIN: 123.45

# Problem 2d: what is the value of w_0  (only your final numeric answer)
PROB_SVMBYHAND_W0: 123.45



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# Answers to Problem 3:  VC Dimension (519 ONLY)
#
# Please list your final answers below for auto-grading.
#
# Your answer can be a number, or an expression in terms of the number of instances
# n or the dimensionality of the space d
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# Problem 3a: What is the VC dimension (a number or equation in terms of n or d)
PROB_VC_SPHERE1: 123.45

# Problem 3b: What is the VC dimension (a number or equation in terms of n or d)
PROB_VC_SPHERE2: 123.45


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# Answer to Implementation Exercise 2.3:  Analysis of the regularization parameter
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# Copy and paste your brief paragraph describing the effect of the regularization 
# parameter from your PDF.
LOGREGIMPLEMENTATION_REGULARIZATION_ANALYSIS:
 As I increased the value of lambda, I observed that...



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# Answer to Implementation Exercise 3.4:  Effect of Kernel Parameters
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# Copy and paste your brief paragraph describing the effect of varying the kernel  
# parameters and C from your PDF.
SVM_KERNELPARAMETEREFFECTS:
  As C increased, I observed that...



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# Answer to Implementation Exercise 3.5:  Optimal SVM parameters
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# What was the optimal value you found for C?
SVM_OPTIMAL_C: 123.45

# What was the optimal value you found for sigma?
SVM_OPTIMAL_SIGMA: 123.45

# What is the estimated accuracy for your choice of optimal parameters?
SVM_OPTIMAL_ACCURACY: 123.45


 
 
 
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# Feedback on the Assignment
# 
# The following information will help us improve future versions of this assignment.
# It is completely optional, but highly appreciated.  Please be honest.
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# Approximately how many hours did it take you to complete this assignment?
FEEDBACK_NUM_HOURS: 0

# Please list any typos / errors you noticed in the assignment description or skeleton code
FEEDBACK_ERRORS: |
    None
 
# Please describe any problems you encountered while completing this assignment
FEEDBACK_PROBLEMS: |
    None