(1) Why linear regression is not good?
(2)
(3)
(4)
(5)
(6)
(7)
Thursday, 21 May 2015
Machine Learning Course 3 Study Log (Linear Algebra Review)
Matrix Inverse:
A * A-1 = I (identity matrix)
Matrix Transpose:
Aij = Aji
A * A-1 = I (identity matrix)
Matrix Transpose:
Aij = Aji
Wednesday, 20 May 2015
Machine Learning Course 1 Study Log (Concept)
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems. In aregression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discretecategories.
Example:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories.
Unsupervised Learning
Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It’s not just about clustering. For example, associative memory is unsupervised learning.
Friday, 24 April 2015
English writing: TRANSITION WORDS
From: https://www.msu.edu/~jdowell/135/transw.html
1:
and, so, also, moreover, furthermore, additionally, actually, in addition, what is more, as well, alternatively.
Introduction:
for example, for instance, such as, in particular, to illustrate,
Reference:
considering, regarding, as for, the fact that, with regards to,
Similarity:
similarly, likewise,
2:
but, however, in contrast, conversely, still, though, yet, on the other hand, while
Emphasis:
even more, above all, indeed, more importantly,
Concession:
even so, nevertheless, admittedly, despite, although, regardless,
replacement:
rather, instead
3:
Cause/Reason:
because, due to, for, as, since
Condition:
if, even if, on the condition that, as long as, provided that, only if, in case,
Effect/Result:
as a result, consequently, hence, so that, so, as a consequence, therefore, thus, accordingly,
Purpose:
for the purpose of, with this in mind, in order that, so as to, so that, in order to, to the end that
Consequence:
then, if so, if not, otherwise,
4:
First, to begin with, at first, to start with, for a start, first of all, secondly
Continuation:
next, afterwards, previously, eventually, then, after this, before, subsequently,
Conclusion:
to conclude, in summary, finally, at last, in the end, lastly, as a final point,
all in all, overall, in short, in all, to put it briefly,
Resumption:
anyway, anyhow
1:
Additive Transitions
Addition:and, so, also, moreover, furthermore, additionally, actually, in addition, what is more, as well, alternatively.
Introduction:
for example, for instance, such as, in particular, to illustrate,
Reference:
considering, regarding, as for, the fact that, with regards to,
Similarity:
similarly, likewise,
2:
Adversative Transitions
Conflict:but, however, in contrast, conversely, still, though, yet, on the other hand, while
Emphasis:
even more, above all, indeed, more importantly,
Concession:
even so, nevertheless, admittedly, despite, although, regardless,
replacement:
rather, instead
3:
Causal Transitions
Cause/Reason:
because, due to, for, as, since
Condition:
if, even if, on the condition that, as long as, provided that, only if, in case,
Effect/Result:
as a result, consequently, hence, so that, so, as a consequence, therefore, thus, accordingly,
Purpose:
for the purpose of, with this in mind, in order that, so as to, so that, in order to, to the end that
Consequence:
then, if so, if not, otherwise,
4:
Sequential Transitions:
Numerical:First, to begin with, at first, to start with, for a start, first of all, secondly
Continuation:
next, afterwards, previously, eventually, then, after this, before, subsequently,
Conclusion:
to conclude, in summary, finally, at last, in the end, lastly, as a final point,
all in all, overall, in short, in all, to put it briefly,
Resumption:
anyway, anyhow
English Writing - Sentence
Subject + Predicate
From: https://courses.edx.org/courses/BerkeleyX/ColWri_2.1x/3T2014/courseware/801692fcdc2a42ea8bec71e876225b3a/ff39b23897e74e3bb8b93dff56e32301/
Making sentence beautiful?
1: Clear
2: Concise
3: Strong verbs
4: Precise vocabulary
5: Length
How to achieve "Concise"?
1: No redundant
2: Watch out for wordy phrases
3: Make your subject clear and defined
4: Use strong verbs
5: Avoid vague words
6: Remove unnecessary modifiers
Bad examples:
1: ten p.m. (at night)
2: to combine (together)
3: What we found was a solution to the problem.
-> We solved the problem
From: https://courses.edx.org/courses/BerkeleyX/ColWri_2.1x/3T2014/courseware/801692fcdc2a42ea8bec71e876225b3a/ff39b23897e74e3bb8b93dff56e32301/
Making sentence beautiful?
1: Clear
2: Concise
3: Strong verbs
4: Precise vocabulary
5: Length
How to achieve "Concise"?
1: No redundant
2: Watch out for wordy phrases
3: Make your subject clear and defined
4: Use strong verbs
5: Avoid vague words
6: Remove unnecessary modifiers
Bad examples:
1: ten p.m. (at night)
2: to combine (together)
3: What we found was a solution to the problem.
-> We solved the problem
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