ml page 1 - indian institute of technology delhianupam/ml_notes.pdf- 1998, tom mitchell, machine...
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Random Variables, Expectation, Variance, Conditional Distribution, Baye's Rule, Normal Distribution, Joint Distribution
Basic Calculus2.
Partial/Double Derivatives, Maxima, Minima
Linear Algebra3.
Matrices, Eigen Values (will cover), Vectors, Hyperplanes, convex optimization
Some background material is present on the website. Convex sets, convex functions, global/local optima
References: Class notes, Andrew Ng material
1998, Tom Mitchell, Machine Learning-
Tuda & Hart, Pattern Classification-
Christopher Bishop - Pattern Recognition and Machine Learning-
2012, K Murphy, Machine Learning - A probabilistic perspective-
2012, Peter Flach, ML - Art and Science of algos that make sense of data-
Books:
Saturday, January 7, 2017 4:00 AM
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Basics of MLSaturday, January 7, 2017 12:11 PM
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Know basic Machine Learning concepts/framework1.Various kinds of Machine Learning algorithms (8-10 algos) (Supervised/Unsupervised)2.Hands on experience with ML algorithms3.Art and Science behinc ML Systems4.
Things from the course:
Types of LearningSunday, January 8, 2017 5:19 PM
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Wednesday, January 18, 2017 8:13 PM
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Friday, January 20, 2017 10:29 PM
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Friday, January 20, 2017 10:55 PM
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Sunday, January 22, 2017 11:04 AM
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Sunday, January 22, 2017 11:50 AM
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Sunday, January 29, 2017 12:24 PM
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7
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Sunday, January 29, 2017 5:52 PM
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Analytical Solution to gradient descentMonday, January 30, 2017 7:28 PM
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Gaussian Discriminant Analysis (GDA)Monday, January 30, 2017 7:59 PM
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Sunday, January 22, 2017 11:52 AM
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Q1. a. [ 4.58687457 5.83129479]
Learning Rate = 0.001
Stopping Criteria = cost(Theta) - cost(ThetaNew) < 0.000001
(b)
c.
d.
Anupam Sobti2015ANZ8497
Assignment ReportFriday, February 10, 2017 8:06 PM
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e.
Observation: We can observe that as increases, the step size increases. After a certain limit, (in this case, ,
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Observation: We can observe that as increases, the step size increases. After a certain limit, (in this case, , the appropriate is never reached.
Q2.a.
Plot for linear regression
Implemented locally weighted linear regression for tao = 0.8
c.
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c.
The value of tao determines the area around a point which is considered in order to determine the piecewise linear model. If tao is too small, the curve starts to overfit and a value too large makes it equivalent to linear regression. The value of tao = 0.3 should work best since it neither fits too much, nor too loose.
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Q3. a.
b.
Q4.a.
Q4.b.
Q4.c.
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Q4.c.
Q4.d.
Q4.e.
f.The linear boundary, due to lack of information about the covariance is similar to what would be the boundary for logistic regression. It merely maximizes the distance of both classes from the boundary. The quadratic boundary however captures the way, change in one parameter changes the other parameter. Therefore, the boundary is able to show the boundary bent towards the Canada class. The reason intuitively can be seen that some of Alaska class candidates are present on the opposite side of the side. Therefore, the probability of Alaska class should be more.
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Monday, February 20, 2017 7:02 AM
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Wednesday, March 1, 2017 9:28 AM
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Wednesday, March 1, 2017 10:02 AM
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The complete framework breaks down in case of an outlier. Therefore, we introduce a slack variable to deal with noise data/outliers.
SVMs with slackWednesday, March 1, 2017 2:49 PM
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Non-linear SVMsWednesday, March 1, 2017 5:28 PM
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* Exam question
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