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![Page 1: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/1.jpg)
Lecture 2 Introduction to ML
Basic Linear Algebra Matlab
Some slides on Linear Algebra are from Patrick Nichols
CS4442/9542b Artificial Intelligence II
Prof. Olga Veksler
![Page 2: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/2.jpg)
Outline
• Introduction to Machine Learning• Basic Linear Algebra • Matlab Intro
![Page 3: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/3.jpg)
Intro: What is Machine Learning?
• How to write a computer program that automatically improves its performance through experience
• Machine learning is useful when it is too difficult to come up with a program to perform a desired task
• Make computer to learn by showing examples (most frequently with correct answers)• “supervised” learning or learning with a teacher
• In practice: computer program (or function) which has a tunable parameters, tune parameters until the desirable behavior on the examples
![Page 4: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/4.jpg)
Different Types of Learning • Learning from examples:
• Supervised Learning: given training examples of inputs and corresponding outputs, produce the “correct” outputs for new inputs
• study in this course • Unsupervised Learning: given only inputs as
training, find structure in the world: e.g. discover clusters
• Other types, such as reinforcement learning are not covered in this course
![Page 5: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/5.jpg)
Supervised Machine Learning
• Training samples (or examples) x1,x2,…, xn
• Each example xi is typically multi-dimensional• xi
1, xi2 ,…, xi
d are called features, xi is often called a feature vector
• Example: x1 = {3,7, 35}, x2 = {5, 9, 47}, …• how many and which features do we take?
• Know desired output for each example y1, y2,…yn
• This learning is supervised (“teacher” gives desired outputs)• yi are often one-dimensional• Example: y1 = 1 (“face”), y2 = 0 (“not a face”)
![Page 6: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/6.jpg)
Supervised Machine Learning
• Two types of supervised learning:• Classification (we will only do classification in this
course): • yi takes value in finite set, typically called a label or
a class • Example: yi {“sunny”, ”cloudy”, ”raining”}
• Regression• yi continuous, typically called an output value• Example: yi = temperature [-60,60]
![Page 7: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/7.jpg)
Toy Application: fish sorting
fish image
fish species salmon
sea bass
sortingchamber
classifier
![Page 8: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/8.jpg)
Classifier design• Notice salmon tends to be shorter than sea bass• Use fish length as the discriminating feature• Count number of bass and salmon of each length
0
2
4
6
8
10
12
2 4 8 10 12 14
Length
Co
un
t
salmon
sea bass
2 4 8 10 12 14
bass 0 1 3 8 10 5
salmon 2 5 10 5 1 0
![Page 9: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/9.jpg)
Single Feature (length) Classifier• Find the best length L threshold
fish length < L fish length > L
classify as salmon classify as sea bass
2 4 8 10 12 14
bass 0 1 3 8 10 5
salmon 2 5 10 5 1 0
• For example, at L = 5, misclassified:
• 1 sea bass• 16 salmon
• Classification error (total error) 1750 = 34%
![Page 10: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/10.jpg)
• After searching through all possible thresholds L, the best L= 9, and still 20% of fish is misclassified
0
2
4
6
8
10
12
2 4 8 10 12 14
Length
Co
un
t
salmon
sea bass
fish classified as salmon
fish classified as sea bass
Single Feature (length) Classifier
![Page 11: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/11.jpg)
Next Step
• Lesson learned:• Length is a poor feature alone!
• What to do?• Try another feature• Salmon tends to be lighter• Try average fish lightness
![Page 12: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/12.jpg)
Single Feature (lightness) Classifier
• Now fish are classified best at lightness threshold of 3.5 with classification error of 8%
0
2
4
6
8
10
12
14
1 2 3 4 5
Lightness
Co
un
t
salmon
sea bass
1 2 3 4 5
bass 0 1 2 10 12
salmon 6 10 6 1 0
![Page 13: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/13.jpg)
bass
salmon
Can do better by feature combining • Use both length and lightness features• Feature vector [length,lightness]
length
light
ness
decision boundary
• Classification error 4%
decision regions
![Page 14: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/14.jpg)
Even Better Decision Boundary
• Decision boundary (wiggly) with 0% classification error
length
lightness
![Page 15: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/15.jpg)
Test Classifier on New Data• The goal is for classifier to perform well on new data• Test “wiggly” classifier on new data: 25% error
length
lightness
![Page 16: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/16.jpg)
What Went Wrong?
• We always have only a limited amount of data, not all possible data
• We should make sure the decision boundary does not adapt too closely to the particulars of the data we have at hand, but rather grasps the “big picture”
added 2 samples
![Page 17: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/17.jpg)
• Complicated boundaries overfit the data, they are too tuned to the particular training data at hand
• Therefore complicated boundaries tend to not generalize well to the new data
• We usually refer to the new data as “test” data
What Went Wrong: Overfitting
![Page 18: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/18.jpg)
Overfitting: Extreme Example• Say we have 2 classes: face and non-face images• Memorize (i.e. store) all the “face” images• For a new image, see if it is one of the stored faces
• if yes, output “face” as the classification result• If no, output “non-face”• also called “rote learning”
• problem: new “face” images are different from stored “face” examples• zero error on stored data, 50% error on test (new) data
• Rote learning is memorization without generalization
slide is modified from Y. LeCun
![Page 19: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/19.jpg)
Generalizationtraining data
• The ability to produce correct outputs on previously unseen examples is called generalization
• The big question of learning theory: how to get good generalization with a limited number of examples
• Intuitive idea: favor simpler classifiers• William of Occam (1284-1347): “entities are not to be multiplied without necessity”
• Simpler decision boundary may not fit ideally to the training data but tends to generalize better to new data
test data
![Page 20: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/20.jpg)
• We can also underfit data, i.e. use too simple decision boundary • chosen model is not expressive enough
• There is no way to fit a linear decision boundary so that the training examples are well separated
• Training error is too high• test error is, of course, also high
Underfitting
![Page 21: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/21.jpg)
Underfitting → Overfitting
underfitting “just right” overfitting
![Page 22: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/22.jpg)
Sketch of Supervised Machine Learning
• Chose a learning machine f(x,w)• w are tunable weights• x is the input sample• f(x,w) should output the correct class of sample x• use labeled samples to tune weights w so that f(x,w)
give the correct label for sample x• Which function f(x,w) do we choose?
• has to be expressive enough to model our problem well, i.e. to avoid underfitting
• yet not to complicated to avoid overfitting
![Page 23: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/23.jpg)
Training and Testing• There are 2 phases, training and testing
• Divide all labeled samples x1,x2,…xn into 2 sets, training set and test set
• Training phase is for “teaching” our machine (finding optimal weights w)
• Testing phase is for evaluating how well our machine works on unseen examples
![Page 24: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/24.jpg)
Training Phase
• Find the weights w s.t. f(xi,w) = yi “as much as possible” for training samples (xi, yi)• “as much as possible” needs to be defined
• How do we find parameters w to ensure f(xi,w) = yi for most training samples (xi,yi) ?• This step is usually done by optimization, can be quite
time consuming
![Page 25: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/25.jpg)
Testing Phase
• The goal is to design machine which performs well on unseen examples
• Evaluate the performance of the trained machine f(x,w) on the test samples (unseen labeled samples)
• Testing the machine on unseen labeled examples lets us approximate how well it will perform in practice
• If testing results are poor, may have to go back to the training phase and redesign f(x,w)
![Page 26: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/26.jpg)
Generalization and Overfitting• Generalization is the ability to produce correct
output on previously unseen examples• In other words, low error on unseen examples• Good generalization is the main goal of ML
• Low training error does not necessarily imply that we will have low test error• we have seen that it is easy to produce f(x,w) which is
perfect on training samples (rote “learning”)• Overfitting
• when the machine performs well on training data but poorly on test data
![Page 27: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/27.jpg)
Classification System Design Overview• Collect and label data by hand
salmon salmon salmonsea bass sea bass sea bass
• Preprocess by segmenting fish from background
• Extract possibly discriminating features• length, lightness, width, number of fins,etc.
• Classifier design• Choose model for classifier• Train classifier on training data
• Test classifier on test data
• Split data into training and test sets
we look at these two steps in this course
![Page 28: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/28.jpg)
Basic Linear Algebra• Basic Concepts in Linear Algebra
• vectors and matrices• products and norms• vector spaces and linear transformations
• Introduction to Matlab
![Page 29: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/29.jpg)
Why Linear Algebra?• For each example (e.g. a fish image), we extract a set
of features (e.g. length, width, color)• This set of features is represented as a feature vector
• [length, width, color]
• Also, we will use linear models since they are simple and computationally tractable
• All collected examples will be represented as collection of (feature) vectors
[l1, w1 , c1 ]
[l2 , w2 , c2 ]
[l3 , w3 , c3 ]
example 1example 2example 3
333
222
111
cwlcwlcwl
matrix
![Page 30: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/30.jpg)
What is a Matrix?• A matrix is a set of elements, organized into
rows and columns
6946
9441
10672
rows
columnsexample 1
example 2
example 3
feat
ure
4
feat
ure
3
feat
ure
2
feat
ure
1
![Page 31: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/31.jpg)
Basic Matrix Operations• addition, subtraction, multiplication by a scalar
hdgc
fbea
hg
fe
dc
ba
hdgc
fbea
hg
fe
dc
ba
add elements
subtract elements
dc
ba
dc
ba
multiply every entry
![Page 32: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/32.jpg)
Matrix Transpose
nmnn
m
m
xxx
xxx
xxx
A
21
22221
11211
nmmm
n
n
T
xxx
xxx
xxx
A
21
22212
12111
T• n by m matrix A and its m by n transpose A
![Page 33: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/33.jpg)
Vectors• Vector: N x 1 matrix
• dot product and magnitude defined on vectors only
2
1
xx
v
x1
v
x2
x1
a
x2
b
vector addition vector subtraction
a+b
x1
a
x2
b
a-b
![Page 34: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/34.jpg)
More on Vectors• n-dimensional row vector nxxxx 21
n
T
x
x
x
x2
1
• Transpose of row vector is column vector
• Vector product (or inner or dot product)
ni
iinnT yxyxyxyxyxyxyx
1
2211,
![Page 35: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/35.jpg)
More on Vectors
yxyx
cosT
• angle q between vectors x and y :
• Euclidian norm or length
niixxxx
1
2,
• If ||x|| =1 we say x is normalized or unit length
• inner product captures direction relationship
0cos
0yxT
yx
x
y
1cos
0 yxyxT
xy
1cos
0 yxyxTx
y
![Page 36: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/36.jpg)
More on Vectors
• Euclidian distance between vectors x and y
ni
ii yxyx1
2
• Vectors x and y are orthonormal if they are orthogonal and ||x|| = ||y|| =1
x
y
x-y
![Page 37: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/37.jpg)
Linear Dependence and Independence
• Vectors x1, x2,…, xn are linearly dependent if there exist constants 1, 2,…, n s.t.
1x1+ 2x2+…+nxn = 0
i ≠ 0 for at least one I
• Vectors x1, x2,…, xn are linearly independent if 1x1+ 2x2+…+nxn = 0 1 = 2=…= n= 0
![Page 38: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/38.jpg)
Vector Spaces and Basis• The set of all n-dimensional vectors is called a
vector space V• A set of vectors {u1,u2,…, un } are called a basis
for vector space if any v in V can be written as v = 1u1+ 2u2+…+nun
• u1,u2,…, un are independent implies they form a basis, and vice versa
• u1,u2,…, un give an orthonormal basis if
1. 2.
iui 1jiuu ji
![Page 39: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/39.jpg)
Orthonormal Basis
0
0
0
zy
zx
yx T
T
T
z
y
x
100
010
001
• x, y,…, z form an orthonormal basis
![Page 40: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/40.jpg)
Matrix Product
ij
dmd
m
m
m
ndnnn
d
c
bb
bbbbbb
aaaa
aaaaAB
1
331
221
111
321
1131211
• # of columns of A = # of rows of B• even if defined, in general AB ≠ BA
cij = ai, bjai is row i of A
bj is column j of B
![Page 41: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/41.jpg)
Matrices• Rank of a matrix is the number of linearly
independent rows (or equivalently columns)• A square matrix is non-singular if its rank equal
to the number of rows. If its rank is less than number of rows it is singular.
• Identity matrix
100
00
010
001
I
AI=IA=A
T• Matrix A is symmetric if A=A
4685
6349
8472
5921
![Page 42: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/42.jpg)
Matrices
• Inverse of a square matrix A is matrix A s.t. AA = I
-1
-1
• If A is singular or not square, inverse does not exist
• Pseudo-inverse A is defined whenever A A is not singular (it is square) A = (A A) A AA =(A A) AA=I
T
T
T
-1
-1
T
T
![Page 43: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/43.jpg)
MATLAB
![Page 44: Lecture 2 Introduction to ML Basic Linear Algebra Matlab Some slides on Linear Algebra are from Patrick Nichols CS4442/9542b Artificial Intelligence II.](https://reader034.fdocuments.net/reader034/viewer/2022050909/56649e375503460f94b26ef5/html5/thumbnails/44.jpg)
• Starting matlab• xterm -fn 12X24• matlab
• Basic Navigation• quit• more • help general
• Scalars, variables, basic arithmetic• Clear• + - * / ^• help arith
• Relational operators• ==,&,|,~,xor• help relop
• Lists, vectors, matrices• A=[2 3;4 5]• A’
• Matrix and vector operations• find(A>3), colon operator• * / ^ .* ./ .^• eye(n),norm(A),det(A),eig(A)• max,min,std• help matfun
• Elementary functions• help elfun
• Data types• double• Char
• Programming in Matlab• .m files• scripts• function y=square(x)• help lang
• Flow control• if i== 1else end, if else if end• for i=1:0.5:2 … end• while i == 1 … end• Return• help lang
• Graphics• help graphics• help graph3d
• File I/O• load,save• fopen, fclose, fprintf, fscanf