02 math essentials
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1) Machine learning draws on areas of mathematics including probability, statistical inference, linear algebra, and optimization theory. 2) While there are easy-to-use machine learning packages, understanding the underlying mathematics is important for choosing the right algorithms, making good parameter and validation choices, and interpreting results. 3) Key concepts in probability and statistics that are important for machine learning include random variables, probability distributions, expected value, variance, covariance, and conditional probability. These concepts allow quantification of relationships and uncertainties in data.
![Example of Bayes rule
Marie is getting married tomorrow at an outdoor ceremony in the
desert. In recent years, it has rained only 5 days each year.
Unfortunately,
Unfortunately the weatherman is forecasting rain for tomorrow When
tomorrow.
it actually rains, the weatherman has forecast rain 90% of the time.
When it doesn't rain, he has forecast rain 10% of the time. What is the
probability it will rain on the day of Marie's wedding?
Marie s
Event A: The weatherman has forecast rain.
Event B: It rains.
We know:
– p( B ) = 5 / 365 = 0.0137 [ It rains 5 days out of the year. ]
– p( not B ) = 360 / 365 = 0.9863
– p( A | B ) = 0.9 [ When it rains, the weatherman has forecast
rain 90% of the time. ]
– p( A | not B ) = 0.1 [When it does not rain the weatherman has
01
rain,
forecast rain 10% of the time.]
Jeff Howbert
Introduction to Machine Learning
Winter 2012
40](https://image.slidesharecdn.com/02mathessentials-140305011717-phpapp02/85/02-math-essentials-40-320.jpg)
![Vector projection
Orthogonal projection of y onto x
– Can take place in any space of dimensionality > 2
– Unit vector in direction of x is
y
x / || x ||
– Length of projection of y in
direction of x is
θ
x
|| y || ⋅ cos(θ )
projx( y )
– Orthogonal projection of
y onto x is the vector
projx( y ) = x ⋅ || y || ⋅ cos(θ ) / || x || =
[ ( x ⋅ y ) / || x ||2 ] x (using dot product alternate form)
Jeff Howbert
Introduction to Machine Learning
Winter 2012
54](https://image.slidesharecdn.com/02mathessentials-140305011717-phpapp02/85/02-math-essentials-54-320.jpg)
02 math essentials
- 1. Machine Learning Math Essentials Jeff Howbert Introduction to Machine Learning Winter 2012 1
- 2. Areas of math essential to machine learning Machine learning is part of both statistics and computer science – Probability – Statistical inference – Validation – Estimates of error, confidence intervals Linear algebra Li l b – Hugely useful for compact representation of linear transformations on data – Dimensionality reduction techniques Optimization theory p y Jeff Howbert Introduction to Machine Learning Winter 2012 2
- 3. Why worry about the math? There are lots of easy-to-use machine learning packages out there. After this course, you will know how to apply several of the most general-purpose algorithms. g p p g HOWEVER To get really useful results, you need good mathematical intuitions about ce ta ge e a at e at ca tu t o s certain general machine learning principles, as well as the inner workings of the individual algorithms. Jeff Howbert Introduction to Machine Learning Winter 2012 3
- 4. Why worry about the math? These intuitions will allow you to: – Choose the right algorithm(s) for the problem – Make good choices on parameter settings, validation strategies g – Recognize over- or underfitting – Troubleshoot poor / ambiguous results – Put appropriate bounds of confidence / uncertainty on results – Do a better job of coding algorithms or incorporating them into more complex p g p analysis pipelines Jeff Howbert Introduction to Machine Learning Winter 2012 4
- 5. Notation a∈A |B| || v || ∑ ∫ ℜ ℜn set membership: a is member of set A cardinality: number of items in set B norm: length of vector v summation integral the t f th set of real numbers l b real number space of dimension n n = 2 : plane or 2-space n = 3 : 3- (dimensional) space p yp p n > 3 : n-space or hyperspace Jeff Howbert Introduction to Machine Learning Winter 2012 5
- 6. Notation x, y, z, vector (bold, lower case) u, v A, B, X matrix (bold, upper case) y = f( x ) function (map): assigns unique value in range of y to each value in domain of x dy / dx derivative of y with respect to single y p g variable x y = f( x ) function on multiple variables, i.e. a ( p vector of variables; function in n-space ∂y / ∂xi partial derivative of y with respect to element i of vector x Jeff Howbert Introduction to Machine Learning Winter 2012 6
- 7. The concept of probability Intuition: In some process, several outcomes are possible. When the process is repeated a large number of times, each outcome occurs with a characteristic relative frequency or probability If a particular frequency, probability. outcome happens more often than another outcome, outcome we say it is more probable probable. Jeff Howbert Introduction to Machine Learning Winter 2012 7
- 8. The concept of probability Arises in two contexts: In actual repeated experiments. – Example: You record the color of 1000 cars driving by. 57 of them are green. You estimate the probability of a car being green as 57 / 1000 = 0 0057 0.0057. In idealized conceptions of a repeated process. – Example: You consider the behavior of an unbiased six-sided die. The expected probability of rolling a 5 is 1 / 6 = 0.1667. – Example: You need a model for how people’s heights are distributed. You choose a normal distribution ( (bell-shaped curve) to represent the expected relative p ) p p probabilities. Jeff Howbert Introduction to Machine Learning Winter 2012 8
- 9. Probability spaces A probability space is a random process or experiment with three components: – Ω, the set of possible outcomes O number of possible outcomes = | Ω | = N – F th set of possible events E F, the t f ibl t an event comprises 0 to N outcomes number of possible events = | F | = 2N – P, the probability distribution function mapping each outcome and event to real number between 0 and 1 (th probability of O or E) b t d (the b bilit f probability of an event is sum of probabilities of possible outcomes in event Jeff Howbert Introduction to Machine Learning Winter 2012 9
- 10. Axioms of probability 1. Non-negativity: for any event E ∈ F p( E ) ≥ 0 F, 2. 2 All possible outcomes: p( Ω ) = 1 3. Additivity of disjoint events: for all events E, E’ ∈ F where E ∩ E’ = ∅, p( E U E’ ) = p( E ) + p( E’ ) Jeff Howbert Introduction to Machine Learning Winter 2012 10
- 11. Types of probability spaces Define | Ω | = number of possible outcomes Discrete space | Ω | is finite – Analysis involves summations ( ∑ ) Continuous space | Ω | is infinite C ti i i fi it – Analysis involves integrals ( ∫ ) Jeff Howbert Introduction to Machine Learning Winter 2012 11
- 12. Example of discrete probability space Single roll of a six-sided die – 6 possible outcomes: O = 1, 2, 3, 4, 5, or 6 p , , , , , – 26 = 64 possible events example: E = ( O ∈ { 1, 3, 5 } ), i.e. outcome is odd – If die is fair, then probabilities of outcomes are equal p( 1 ) = p( 2 ) = p( 3 ) = p( 4 ) = p( 5 ) = p( 6 ) = 1 / 6 example: probability of event E = ( outcome is odd ) is p( 1 ) + p( 3 ) + p( 5 ) = 1 / 2 Jeff Howbert Introduction to Machine Learning Winter 2012 12
- 13. Example of discrete probability space Three consecutive flips of a coin – 8 possible outcomes: O = HHH, HHT, HTH, HTT, p , , , , THH, THT, TTH, TTT – 28 = 256 possible events example: E = ( O ∈ { HHT, HTH, THH } ), i.e. exactly two flips are heads example: E = ( O ∈ { THT, TTT } ), i.e. the first and third flips are tails – If coin is fair, then probabilities of outcomes are equal p( HHH ) = p( HHT ) = p( HTH ) = p( HTT ) = p( THH ) = p( THT ) = p( TTH ) = p( TTT ) = 1 / 8 example: probability of event E = ( exactly two heads ) is p( HHT ) + p( HTH ) + p( THH ) = 3 / 8 ( ( ( Jeff Howbert Introduction to Machine Learning Winter 2012 13
- 14. Example of continuous probability space Height of a randomly chosen American male – Infinite number of possible outcomes: O has some single value in range 2 feet to 8 feet – Infinite number of possible events example: E = ( O | O < 5.5 feet ), i.e. individual chosen is less than 5.5 feet tall – Probabilities of outcomes are not equal and are equal, described by a continuous function, p( O ) Jeff Howbert Introduction to Machine Learning Winter 2012 14
- 15. Example of continuous probability space Height of a randomly chosen American male – Probabilities of outcomes O are not equal and are equal, described by a continuous function, p( O ) – p( O ) is a relative, not an absolute probability p( O ) for any particular O is zero ∫ p( O ) from O = -∞ to ∞ (i.e. area under curve) is 1 example: p( O = 5 8 ) > p( O = 6 2 ) 5’8” 6’2” example: p( O < 5’6” ) = ( ∫ p( O ) from O = -∞ to 5’6” ) ≈ 0.25 Jeff Howbert Introduction to Machine Learning Winter 2012 15
- 16. Probability distributions Discrete: probability mass function (pmf) example: sum of two fair dice probability density function (pdf) example: waiting time between eruptions of Old Faithful (minutes) Jeff Howbert probability Continuous: Introduction to Machine Learning Winter 2012 16
- 17. Random variables A random variable X is a function that associates a number x with each outcome O of a process – C Common notation: X( O ) = x, or j t X = x t ti just Basically a way to redefine (usually simplify) a probability space to a new probability space – X must obey axioms of probability (over the possible values of x) – X can be discrete or continuous Example: X = number of heads in three flips of a coin – Possible values of X are 0, 1, 2, 3 – p( X = 0 ) = p( X = 3 ) = 1 / 8 p( X = 1 ) = p( X = 2 ) = 3 / 8 – Size of space (number of “outcomes”) reduced from 8 to 4 Example: X = average height of five randomly chosen American men – Size of space unchanged (X can range from 2 feet to 8 feet) but feet), pdf of X different than for single man Jeff Howbert Introduction to Machine Learning Winter 2012 17
- 18. Multivariate probability distributions Scenario – Several random processes occur ( p (doesn’t matter whether in parallel or in sequence) – Want to know probabilities for each possible combination of outcomes bi ti f t Can describe as joint probability of several random variables – Example: two processes whose outcomes are represented by random variables X and Y. Probability that process X has outcome x and process Y has outcome y is denoted as: p( X = x, Y = y ) ( Jeff Howbert Introduction to Machine Learning Winter 2012 18
- 19. Example of multivariate distribution joint probability: p( X = minivan, Y = European ) = 0.1481 Jeff Howbert Introduction to Machine Learning Winter 2012 19
- 20. Multivariate probability distributions Marginal probability – Probability distribution of a single variable in a joint distribution – Example: two random variables X and Y: p( X = x ) = ∑b=all values of Y p( X = x, Y = b ) Conditional probability – Probability distribution of one variable given that another variable takes a certain value – Example: two random variables X and Y: p( X = x | Y = y ) = p( X = x Y = y ) / p( Y = y ) x, Jeff Howbert Introduction to Machine Learning Winter 2012 20
- 21. Example of marginal probability marginal probability: p( X = minivan ) = 0.0741 + 0.1111 + 0.1481 = 0.3333 Jeff Howbert Introduction to Machine Learning Winter 2012 21
- 22. Example of conditional probability conditional probability: p( Y = European | X = minivan ) = 0.1481 / ( 0.0741 + 0.1111 + 0.1481 ) = 0.4433 p probability 0.2 0.15 0 15 0.1 0.05 0 American sport Asian Y = manufacturer Jeff Howbert SUV minivan European sedan Introduction to Machine Learning X = model type Winter 2012 22
- 23. Continuous multivariate distribution Same concepts of joint, marginal, and conditional probabilities apply (except use integrals) Example: three-component Gaussian mixture in two dimensions Jeff Howbert Introduction to Machine Learning Winter 2012 23
- 24. Expected value Given: A discrete random variable X with possible X, values x = x1, x2, … xn Probabilities p( X = xi ) that X takes on the various values of xi A function yi = f( xi ) defined on X The expected value of f is the probability-weighted “average” value of f( xi ): E( f ) = ∑i p( xi ) ⋅ f( xi ) Jeff Howbert Introduction to Machine Learning Winter 2012 24
- 25. Example of expected value Process: game where one card is drawn from the deck – If face card, dealer pays y $10 , p y you $ – If not a face card, you pay dealer $4 Random variable X = { face card, not face card } – p( face card ) = 3/13 – p( not face card ) = 10/13 Function f( X ) is payout to you – f( face card ) = 10 – f( not face card ) = -4 tf d 4 Expected value of payout is: E( f ) = ∑i p( xi ) ⋅ f( xi ) = 3/13 ⋅ 10 + 10/13 ⋅ -4 = -0.77 4 0 77 Jeff Howbert Introduction to Machine Learning Winter 2012 25
- 26. Expected value in continuous spaces E( f ) = ∫x = a → b p( x ) ⋅ f( x ) Jeff Howbert Introduction to Machine Learning Winter 2012 26
- 27. Common forms of expected value (1) Mean (μ) f( xi ) = xi ⇒ μ = E( f ) = ∑i p( xi ) ⋅ xi – Average value of X = xi, taking into account probability of the various xi – M t common measure of “center” of a distribution Most f“ t ” f di t ib ti Compare to formula for mean of an actual sample 1 n μ = ∑ xi N i =1 Jeff Howbert Introduction to Machine Learning Winter 2012 27
- 28. Common forms of expected value (2) Variance (σ2) f( xi ) = ( xi - μ ) ⇒ σ2 = ∑i p( xi ) ⋅ ( xi - μ )2 – Average value of squared deviation of X = xi from mean μ, taking into account probability of the various xi – M t common measure of “spread” of a distribution Most f“ d” f di t ib ti – σ is the standard deviation Compare to formula for variance of an actual sample 1 n 2 σ = ( xi − μ ) 2 ∑ N − 1 i =1 Jeff Howbert Introduction to Machine Learning Winter 2012 28
- 29. Common forms of expected value (3) Covariance f( xi ) = ( xi - μx ), g( yi ) = ( yi - μy ) ⇒ cov( x y ) = ∑i p( xi , yi ) ⋅ ( xi - μx ) ⋅ ( yi - μy ) x, high (pos sitive) covaria ance no co ovariance – Measures tendency for x and y to deviate from their means in same (or opposite) directions at same time Compare to formula for covariance of actual samples 1 n cov( x, y ) = ∑ ( xi − μ x )( yi − μ y ) N − 1 i =1 Jeff Howbert Introduction to Machine Learning Winter 2012 29
- 30. Correlation Pearson’s correlation coefficient is covariance normalized by the standard deviations of the two variables cov( x, y ) corr( x, y ) = σ xσ y – Always lies in range -1 to 1 – Only reflects linear dependence between variables Linear dependence with noise Linear dependence without noise Various nonlinear dependencies Jeff Howbert Introduction to Machine Learning Winter 2012 30
- 31. Complement rule Given: event A, which can occur or not p( not A ) = 1 - p( A ) Ω A not A areas represent relative probabilities Jeff Howbert Introduction to Machine Learning Winter 2012 31
- 32. Product rule Given: events A and B, which can co-occur (or not) p( A B ) = p( A | B ) ⋅ p( B ) A, (same expression given previously to define conditional probability) (not A, not B) A (A B) A, B Ω (A, not B) (not A, B) areas represent relative probabilities Jeff Howbert Introduction to Machine Learning Winter 2012 32
- 33. Example of product rule Probability that a man has white hair (event A) and is over 65 (event B) – p( B ) = 0.18 – p( A | B ) = 0.78 – p( A, B ) = p( A | B ) ⋅ p( B ) = 0.78 ⋅ 0.18 = 0.14 Jeff Howbert Introduction to Machine Learning Winter 2012 33
- 34. Rule of total probability Given: events A and B, which can co-occur (or not) p( A ) = p( A B ) + p( A not B ) A, A, (same expression given previously to define marginal probability) (not A, not B) A (A B) A, B Ω (A, not B) (not A, B) areas represent relative probabilities Jeff Howbert Introduction to Machine Learning Winter 2012 34
- 35. Independence Given: events A and B, which can co-occur (or not) p( A | B ) = p( A ) or p( A B ) = p( A ) ⋅ p( B ) A, Ω (not A, not B) (not A, B) B (A, not B) A ( A, B ) areas represent relative probabilities Jeff Howbert Introduction to Machine Learning Winter 2012 35
- 36. Examples of independence / dependence Independence: – Outcomes on multiple rolls of a die p – Outcomes on multiple flips of a coin – Height of two unrelated individuals – Probability of getting a king on successive draws from a deck, if card from each draw is replaced Dependence: D d – Height of two related individuals – Duration of successive eruptions of Old Faithful – Probability of getting a king on successive draws from a deck, if card from each draw is not replaced , p Jeff Howbert Introduction to Machine Learning Winter 2012 36
- 37. Example of independence vs. dependence Independence: All manufacturers have identical product mix. p( X = x | Y = y ) = p( X = x ). Dependence: American manufacturers love SUVs, Europeans manufacturers don’t. Jeff Howbert Introduction to Machine Learning Winter 2012 37
- 38. Bayes rule A way to find conditional probabilities for one variable when conditional probabilities for another variable are known. p( B | A ) = p( A | B ) ⋅ p( B ) / p( A ) where p( A ) = p( A, B ) + p( A, not B ) (not A, not B) A ( A, B ) B Ω (A, not B) Jeff Howbert (not A, B) Introduction to Machine Learning Winter 2012 38
- 39. Bayes rule posterior probability ∝ likelihood × prior probability p( B | A ) = p( A | B ) ⋅ p( B ) / p( A ) (not A, not B) A ( A, B ) B Ω (A, not B) Jeff Howbert (not A, B) Introduction to Machine Learning Winter 2012 39
- 40. Example of Bayes rule Marie is getting married tomorrow at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, Unfortunately the weatherman is forecasting rain for tomorrow When tomorrow. it actually rains, the weatherman has forecast rain 90% of the time. When it doesn't rain, he has forecast rain 10% of the time. What is the probability it will rain on the day of Marie's wedding? Marie s Event A: The weatherman has forecast rain. Event B: It rains. We know: – p( B ) = 5 / 365 = 0.0137 [ It rains 5 days out of the year. ] – p( not B ) = 360 / 365 = 0.9863 – p( A | B ) = 0.9 [ When it rains, the weatherman has forecast rain 90% of the time. ] – p( A | not B ) = 0.1 [When it does not rain the weatherman has 01 rain, forecast rain 10% of the time.] Jeff Howbert Introduction to Machine Learning Winter 2012 40
- 41. Example of Bayes rule, cont’d. 1. 2. 3. We want to know p( B | A ), the probability it will rain on the day of Marie's wedding, given a forecast for rain by the th weatherman. Th answer can b d t th The be determined f i d from Bayes rule: p( B | A ) = p( A | B ) ⋅ p( B ) / p( A ) p( A ) = p( A | B ) ⋅ p( B ) + p( A | not B ) ⋅ p( not B ) = (0.9)(0.014) + (0.1)(0.986) = 0.111 p( B | A ) = (0.9)(0.0137) / 0.111 = 0.111 The result seems unintuitive but is correct. Even when the weatherman predicts rain, it only rains only about 11% of p gloomy p y prediction, it , the time. Despite the weatherman's g is unlikely Marie will get rained on at her wedding. Jeff Howbert Introduction to Machine Learning Winter 2012 41
- 42. Probabilities: when to add, when to multiply ADD: When you want to allow for occurrence of any of several possible outcomes of a single process. Comparable to logical OR. MULTIPLY: When you want to allow for simultaneous occurrence of particular outcomes p from more than one process. Comparable to logical AND. – But only if the processes are independent. Jeff Howbert Introduction to Machine Learning Winter 2012 42
- 43. Linear algebra applications 1) 2) 3) 4) 5) 6) Operations on or between vectors and matrices Coordinate transformations Dimensionality reduction Linear regression Solution of linear systems of equations Many others M th Applications 1) – 4) are directly relevant to this course. Today we’ll start with 1). Jeff Howbert Introduction to Machine Learning Winter 2012 43
- 44. Why vectors and matrices? Most common form of data organization for machine learning is a 2D array, where – rows represent samples p p (records, items, datapoints) – columns represent attributes p (features, variables) Natural to think of each sample as a vector of attributes, and whole array as a matrix Jeff Howbert Introduction to Machine Learning vector Refund Marital Status Taxable Income Cheat Yes Single 125K No No Married 100K No No Single 70K No Yes Married 120K No No Divorced 95K Yes No Married No Yes Divorced 220K No No Single 85K Yes No Married 75K No No Single 90K Yes 60K 10 matrix Winter 2012 44
- 45. Vectors Definition: an n-tuple of values (usually real numbers). – n referred to as the dimension of the vector – n can be any positive integer from 1 to infinity integer, Can be written in column form or row form – Column form is conventional – Vector elements referenced by subscript ⎛ x1 ⎞ ⎜ ⎟ x=⎜ M ⎟ ⎜x ⎟ ⎝ n⎠ Jeff Howbert x T = ( x1 L xn ) T means " t transpose" " Introduction to Machine Learning Winter 2012 45
- 46. Vectors Can think of a vector as: – a point in space or – a directed line segment with a magnitude and direction Jeff Howbert Introduction to Machine Learning Winter 2012 46
- 47. Vector arithmetic Addition of two vectors – add corresponding elements z = x + y = (x1 + y1 L xn + yn ) T – result is a vector Scalar multiplication of a vector – multiply each element by scalar y = ax = (a x1 L axn ) T – result is a vector Jeff Howbert Introduction to Machine Learning Winter 2012 47
- 48. Vector arithmetic Dot product of two vectors – multiply corresponding elements, th add products lti l di l t then dd d t n a = x ⋅ y = ∑ xi yi i =1 – result is a scalar y Dot product alternative form a = x ⋅ y = x y cos (θ ) Jeff Howbert Introduction to Machine Learning θ x Winter 2012 48
- 49. Matrices Definition: an m x n two-dimensional array of values (usually real numbers). – m rows – n columns Matrix referenced by two-element subscript – first element in ⎛ a11 L a1n ⎞ subscript is row ⎜ ⎟ A=⎜ M O M ⎟ – second element in ⎜a L amn ⎟ ⎝ m1 ⎠ subscript is column – example: A24 or a24 is element in second row, fourth column of A Jeff Howbert Introduction to Machine Learning Winter 2012 49
- 50. Matrices A vector can be regarded as special case of a matrix, where one of matrix dimensions = 1. Matrix transpose (denoted T) – swap columns and rows row 1 becomes column 1, etc. – m x n matrix becomes n x m matrix – example: 4 ⎞ ⎛2 ⎛ 2 7 − 1 0 3⎞ A=⎜ ⎜ 4 6 − 3 1 8⎟ ⎟ ⎝ ⎠ Jeff Howbert ⎟ ⎜ 6 ⎟ ⎜7 AT = ⎜ − 1 − 3⎟ ⎜ ⎟ 1 ⎟ ⎜0 ⎜3 8 ⎟ ⎝ ⎠ Introduction to Machine Learning Winter 2012 50
- 51. Matrix arithmetic Addition of two matrices – matrices must be same size – add corresponding elements: cij = aij + bij – result is a matrix of same size C= A+B = ⎛ a11 + b11 L a1n + b1n ⎞ ⎜ ⎟ M O M ⎜ ⎟ ⎜a + b L amn + bmn ⎟ m1 m1 ⎝ ⎠ Scalar multiplication of a matrix – multiply each element by scalar: bij = d ⋅ aij – result is a matrix of same size Jeff Howbert Introduction to Machine Learning B = d ⋅A = ⎛ d ⋅ a11 L d ⋅ a1n ⎞ ⎜ ⎟ O M ⎟ ⎜ M ⎜d ⋅a L d ⋅ amn ⎟ m1 ⎝ ⎠ Winter 2012 51
- 52. Matrix arithmetic Matrix-matrix multiplication – vector-matrix multiplication j p just a special case p TO THE BOARD!! Multiplication is associative A⋅(B⋅C)=(A⋅B)⋅C Multiplication is not commutative A ⋅ B ≠ B ⋅ A (generally) Transposition rule: ( A ⋅ B )T = B T ⋅ A T Jeff Howbert Introduction to Machine Learning Winter 2012 52
- 53. Matrix arithmetic RULE: In any chain of matrix multiplications, the column dimension of one matrix in the chain must match the row dimension of the following matrix in the chain. Examples A3x5 B5x5 C3x1 Right: A ⋅ B ⋅ AT CT ⋅ A ⋅ B AT ⋅ A ⋅ B C ⋅ CT ⋅ A Wrong: A⋅B⋅A C⋅A⋅B A ⋅ AT ⋅ B CT ⋅ C ⋅ A Jeff Howbert Introduction to Machine Learning Winter 2012 53
- 54. Vector projection Orthogonal projection of y onto x – Can take place in any space of dimensionality > 2 – Unit vector in direction of x is y x / || x || – Length of projection of y in direction of x is θ x || y || ⋅ cos(θ ) projx( y ) – Orthogonal projection of y onto x is the vector projx( y ) = x ⋅ || y || ⋅ cos(θ ) / || x || = [ ( x ⋅ y ) / || x ||2 ] x (using dot product alternate form) Jeff Howbert Introduction to Machine Learning Winter 2012 54
- 55. Optimization theory topics Maximum likelihood Expectation maximization Gradient descent Jeff Howbert Introduction to Machine Learning Winter 2012 55