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
ML Page 1
Basics of MLSaturday, January 7, 2017 12:11 PM
ML Page 2
ML Page 3
ML Page 4
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
ML Page 5
ML Page 6
ML Page 7
ML Page 8
Wednesday, January 18, 2017 8:13 PM
ML Page 9
ML Page 10
ML Page 11
ML Page 12
ML Page 13
ML Page 14
ML Page 15
ML Page 16
ML Page 17
ML Page 18
Friday, January 20, 2017 10:29 PM
ML Page 19
ML Page 20
Friday, January 20, 2017 10:55 PM
ML Page 21
ML Page 22
Sunday, January 22, 2017 11:04 AM
ML Page 23
ML Page 24
Sunday, January 22, 2017 11:50 AM
ML Page 25
ML Page 26
ML Page 27
Sunday, January 29, 2017 12:24 PM
ML Page 28
7
ML Page 29
ML Page 30
Sunday, January 29, 2017 5:52 PM
ML Page 31
ML Page 32
Analytical Solution to gradient descentMonday, January 30, 2017 7:28 PM
ML Page 33
ML Page 34
Gaussian Discriminant Analysis (GDA)Monday, January 30, 2017 7:59 PM
ML Page 35
ML Page 36
ML Page 37
ML Page 38
ML Page 39
ML Page 40
ML Page 41
Sunday, January 22, 2017 11:52 AM
ML Page 42
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
ML Page 43
e.
Observation: We can observe that as increases, the step size increases. After a certain limit, (in this case, ,
ML Page 44
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.
ML Page 45
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.
ML Page 46
Q3. a.
b.
Q4.a.
Q4.b.
Q4.c.
ML Page 47
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.
ML Page 48
Monday, February 20, 2017 7:02 AM
ML Page 49
ML Page 50
ML Page 51
ML Page 52
ML Page 53
Wednesday, March 1, 2017 9:28 AM
ML Page 54
ML Page 55
Wednesday, March 1, 2017 10:02 AM
ML Page 56
ML Page 57
ML Page 58
ML Page 59
ML Page 60
ML Page 61
ML Page 62
ML Page 63
ML Page 64
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
ML Page 65
ML Page 66
Non-linear SVMsWednesday, March 1, 2017 5:28 PM
ML Page 67
ML Page 68
* Exam question
ML Page 69