CSE 432 Midterm

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machine learning is the study of _________ that improve their __________ at some ________ with ___________

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machine learning is the study of _________ that improve their __________ at some ________ with ___________

algorithms; performance; task; experience

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well-defined learning task: < ____ >

P, T, E

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machine learning is good at recognizing ________, recognizing _________, and _________

patterns; anomalies; prediction

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deep learning is a type of __________ ________ _________

artificial neural network

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more than 2 hidden layers makes it a _____ _______ _________ (____)

deep neural network (DNN)

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the most popular machine learning algorithm

deep learning

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the ______ in Python is important because it indicates a block of code

indentation

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comments in python use ___; block comments use three ___ or ___

#; ‘; “

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variables in python are _____ _________ and must start with a ______ or the ________ character, no _________

case sensitive; letter; underscore; numbers

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boolean in Python is declared as _______

bool

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Python for loop syntax through myList

for x in myList:

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Python for loop syntax for range of 10

for x in range(10):

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used in Python to store data values in key:value pairs; they are _______ and do not allow ________

dictionaries; ordered; duplicates

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Python function definition

def myFunction(input):

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what do you add to you parameter for an arbitrary number of arguments?

*

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declare arr [1 2 3 4] as an numpy array

arr = np.array([1, 2, 3, 4])

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declare 2×2 matrix (mat) as numpy array

mat = np.array([1,1],[2,2])

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check the dimension of numpy array (arr) TWO WAYS

arr.ndim; arr.shape

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comprehensive library for creating static, animated, and interactive visualizations in Python

matplotlib

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a vector is a ____ _____

1D array

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matrix transpose is an operator that _____ the matrix over its _______, in turn switching the _____ and ______

flips; diagonal; rows; columns

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v = [a,b]
f(v) = a² + b²
what is f’(v) with respect to v?

f’(v) = [2a, 2b]

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for derivatives with a matrix or vector, we normally multiply the ________ and the ____ ________

transpose; one vector

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python code to find magnitude of vector x

y = x**2
s = np.sum(y)
d = np.sqrt(s)

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python add/subtract vectors x and y

x + y; x - y

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numpy dot product for x and y

np.dot(x,y)

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matplotlib plot function for x, y

plt.plot(x, y, label='My Plot’, linewidth=2.0)

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KNN is ___ __________ _________

non parameter learning

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non-parameter learning y = _____
parameter learning y = ____

f(X, X_train); f(X,W)

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non-parameter learning needs the _____ ________ ________ and is very slow in ______ with almost no ______ ________

entire training dataset; inferring; training process

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similar to using a dictionary to find definitions or synonyms

non-parameter learning

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parameter learning requires the ____, is very _____ in _______, but takes more ______ in _______

weight; fast; inferring; time; training

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similar to having the word in your brain to recognize it at once

parameter learning

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gives you the ground truth

loss function

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common loss function

Loss(y, y^) = sum(y-y^)²

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with different combinations of theta0 and theta1, we obtain different ______ ______, it is a ____ surface

loss values; 3D

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loss value shows how close your _________ __________ ___________ is to the ________ ________

machine learning algorithm; ground truth

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for a loss value, the _______ the ________

lower; better

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machine learning aims to find the best ________ that ____ ________ could obtain the _______ value

parameters; loss function; lowest

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how do we get the smallest loss value

gradient descent

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each step of gradient descent uses all of the training examples - this is known as …

batch gradient descent

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your step size in gradient descent is known as the _________ _____

learning rate

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output is decrete in _________

classification

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output is continuous in _________

regression

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machine learning is a ____-______ approach

data driven

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data: any __________ fact, value, text, sound, or picture not being _______ and __________

unprocessed; interpreted; analyzed

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a set of data collected for machine learning based task

dataset

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a set of data used to discover predictive relationships

training dataset

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a set of data used to asses the strength and utility of a predictive relationship

test dataset

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the attributes to each data sample

features

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KNN stands for:

k nearest neighbors

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for KNN:

  1. calculate the ____ _________ for every _____ _______

  2. select the ____ data points with the _________ _________

  3. ________ based on the k point (new data point should belong to same category as the _______ )

L-2 distance; data point; K; smallest distance; voting; majority

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can you still use KNN if there is more than one feature for distance calculations?

yes

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when setting up KNN, you can choose two parameters:

  1. the best ______ of ___ for ________

  2. the best _______ for ________

value; k; voting; distance; measuring

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the parameters you set of KNN are known as _____________ and are not ________ by the machine learning _______ itself

hyperparameters; adapted; algorithm

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a set of examples used to tune the hyperparameters

validation dataset

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never use _____ data to _____ _______

test; train model

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cross validation: when dataset is ______, ______ data, try each fold as _______ and _______

small; split; validation; average

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cross validation is _________ in deep learning

uncommon

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learning from labeled examples

supervised learning

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draw from inferences from datasets consisting of input data without labeled responses

unsupervised learning

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supervised learning has pairs with an ______ object and a desired ______ value

input; ouput

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unsupervised learning finds ______ ________ or _________ in data

hidden patterns; grouping

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K-Means Algorithm:

  1. initialize ____ _______ _______

  2. assign _____ ______ to ________ clusters

  3. update _______ _________ by calculating _________

  4. repeat ___ and ___ until _________

  5. select optimal number of ________

K center centroids; data points; nearest; center centroids; average; 2; 3; convergence; clusters

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non-parameter learning requires computation of all of the _______ ________, taking more ______ and ________

training dataset; time; memory

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non-parameter/parameter, supervised/unsupervised
KNN:
K-Means:
Linear Regression:

non-parameter, supervised; non-parameter, unsupervised; parameter, supervised

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KNN and K-Means are __________ tasks whereas linear regression is a _________ task

classification; regression

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linear regression steps

propose model; gradient descent; get parameters and test

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image recognition is _______; stock price prediction is ________

classification; regression

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softmax classifier: build upon ________ _________; _____ score of class k to __________ of being in this class; __________ of being in different classes sum up to ____

linear classification; map; probability; probabilities; 1

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loss over the dataset is the _________ ______ for all _________

average loss; examples

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three loss functions

MAE; MSE; Cross Entropy

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MAE: ______ ________ __________
Equation:

mean absolution error; abs(y^ - y)

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MSE: ______ _________ _________
Equation:

mean square error; (y^ - y)²

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Cross Entropy is the _________ _____ likelihood of the __________ ________ as the loss

negative log; correct class

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cross entropy for the following:
true label: [1 0 0 0 0]
softmax: [0.1 0.5 0.1 0.1 0.2]

-(1*log(0.1))+(0*log(0.5))+(0*log(0.1))+(0*log(0.1))+(0*log(0.2))

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Regularization:
- it is likely different ___ has the same _____
- regularization helps to _______ ________ and avoid _________

W; loss; express preference; overfitting

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L(W) including regularization

L(W) = data loss + regularization

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overfitting: model tries to fit not only the __________ relation between _____ and ______ but also the _______ ________; ________ ______________ helps select simple models

regular; inputs; outputs; sampling errors; weight regularization

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numerical gradient: __________, ______, easy to _____
analytic gradient: ______, _______, _______ prone
—> in practice we use _______ but check with _________

approximate; slow; write; exact; fast; error; analytic; numerical

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with backpropogation, given f(x, y, z), you’ll end up getting which derivatives

df/dx; df/dy; df/dz

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in backpropogation, multiply the _________ by the ______ ___________

upstream; local gradient

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tool used for forward and back propogation

computational graph

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the local gradient is the _________

derivative

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