01 and 02: Introduction, Regression Analysis, and Gradient DescentNext: Week 3 Index: Index Introduction to the course  We will learn about
 State of the art
 How to do the implementation
 Applications of machine learning include
 Search
 Photo tagging
 Spam filters
 The AI dream of building machines as intelligent as humans
 Many people believe best way to do that is mimic how humans learn
 What the course covers
 Learn about state of the art algorithms
 But the algorithms and math alone are no good
 Need to know how to get these to work in problems
 Why is ML so prevalent?
 Grew out of AI
 Build intelligent machines
 You can program a machine how to do some simple thing
 For the most part hardwiring AI is too difficult
 Best way to do it is to have some way for machines to learn things themselves
 A mechanism for learning  if a machine can learn from input then it does the hard work for you
Examples Database mining
 Machine learning has recently become so big party because of the huge amount of data being generated
 Large datasets from growth of automation web
 Sources of data include
 Web data (clickstream or click through data)
 Mine to understand users better
 Huge segment of silicon valley
 Medical records
 Electronic records > turn records in knowledges
 Biological data
 Gene sequences, ML algorithms give a better understanding of human genome
 Engineering info
 Data from sensors, log reports, photos etc
 Applications that we cannot program by hand
 Autonomous helicopter
 Handwriting recognition
 This is very inexpensive because when you write an envelope, algorithms can automatically route envelopes through the post
 Natural language processing (NLP)
 AI pertaining to language
 Computer vision
 Self customizing programs
 Netflix
 Amazon
 iTunes genius
 Take users info
 Learn based on your behavior
 Understand human learning and the brain
 If we can build systems that mimic (or try to mimic) how the brain works, this may push our own understanding of the associated neurobiology
What is machine learning? Here we...
 Define what it is
 When to use it
 Not a well defined definition
 Couple of examples of how people have tried to define it
 Arthur Samuel (1959)
 Machine learning: "Field of study that gives computers the ability to learn without being explicitly programmed"
 Samuels wrote a checkers playing program
 Had the program play 10000 games against itself
 Work out which board positions were good and bad depending on wins/losses
 Tom Michel (1999)
 Well posed learning problem: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."
 The checkers example,
 E = 10000s games
 T is playing checkers
 P if you win or not
 Several types of learning algorithms
 Supervised learning
 Teach the computer how to do something, then let it use it;s new found knowledge to do it
 Unsupervised learning
 Let the computer learn how to do something, and use this to determine structure and patterns in data
 Reinforcement learning
 Recommender systems
 This course
 Look at practical advice for applying learning algorithms
 Learning a set of tools and how to apply them
Supervised learning  introduction  Probably the most common problem type in machine learning
 Starting with an example
 How do we predict housing prices
 Collect data regarding housing prices and how they relate to size in feet
 Example problem: "Given this data, a friend has a house 750 square feet  how much can they be expected to get?"
 What approaches can we use to solve this?
 Straight line through data
 Second order polynomial
 One thing we discuss later  how to chose straight or curved line?
 Each of these approaches represent a way of doing supervised learning
 What does this mean?
 We gave the algorithm a data set where a "right answer" was provided
 So we know actual prices for houses
 The idea is we can learn what makes the price a certain value from the training data
 The algorithm should then produce more right answers based on new training data where we don't know the price already
 We also call this a regression problem
 Predict continuous valued output (price)
 No real discrete delineation
 Another example
 Can we definer breast cancer as malignant or benign based on tumor size
 Looking at data
 Five of each
 Can you estimate prognosis based on tumor size?
 This is an example of a classification problem
 Classify data into one of two discrete classes  no in between, either malignant or not
 In classification problems, can have a discrete number of possible values for the output
 e.g. maybe have four values
 0  benign
 1  type 1
 2  type 2
 3  type 4
 In classification problems we can plot data in a different way
 Use only one attribute (size)
 In other problems may have multiple attributes
 We may also, for example, know age and tumor size
 Based on that data, you can try and define separate classes by
 Drawing a straight line between the two groups
 Using a more complex function to define the two groups (which we'll discuss later)
 Then, when you have an individual with a specific tumor size and who is a specific age, you can hopefully use that information to place them into one of your classes
 You might have many features to consider
 Clump thickness
 Uniformity of cell size
 Uniformity of cell shape
 The most exciting algorithms can deal with an infinite number of features
 How do you deal with an infinite number of features?
 Neat mathematical trick in support vector machine (which we discuss later)
 If you have an infinitely long list  we can develop and algorithm to deal with that
 Summary
 Supervised learning lets you get the "right" data a
 Regression problem
 Classification problem
Unsupervised learning  introduction Second major problem type
 In unsupervised learning, we get unlabeled data
 Just told  here is a data set, can you structure it
 One way of doing this would be to cluster data into to groups
 This is a clustering algorithm
Clustering algorithm Example of clustering algorithm
 Google news
 Groups news stories into cohesive groups
 Used in any other problems as well
 Genomics
 Microarray data
 Have a group of individuals
 On each measure expression of a gene
 Run algorithm to cluster individuals into types of people
 Organize computer clusters
 Identify potential weak spots or distribute workload effectively
 Social network analysis
 Astronomical data analysis
 Algorithms give amazing results
 Basically
 Can you automatically generate structure
 Because we don't give it the answer, it's unsupervised learning
Cocktail party algorithm Cocktail party problem
 Lots of overlapping voices  hard to hear what everyone is saying
 Two people talking
 Microphones at different distances from speakers
 Record sightly different versions of the conversation depending on where your microphone is
 But overlapping none the less
 Have recordings of the conversation from each microphone
 Give them to a cocktail party algorithm
 Algorithm processes audio recordings
 Determines there are two audio sources
 Separates out the two sources
 Is this a very complicated problem
 Algorithm can be done with one line of code!
 [W,s,v] = svd((repmat(sum(x.*x,1), size(x,1),1).*x)*x');
 Not easy to identify
 But, programs can be short!
 Using octave (or MATLAB) for examples
 Often prototype algorithms in octave/MATLAB to test as it's very fast
 Only when you show it works migrate it to C++
 Gives a much faster agile development
 Understanding this algorithm
 svd  linear algebra routine which is built into octave
 In C++ this would be very complicated!
 Shown that using MATLAB to prototype is a really good way to do this
Linear Regression  Housing price data example used earlier
 Supervised learning regression problem
 What do we start with?
 Training set (this is your data set)
 Notation (used throughout the course)
 m = number of training examples
 x's = input variables / features
 y's = output variable "target" variables
 (x,y)  single training example
 (x^{i}, y^{j})  specific example (i^{th} training example)
 i is an index to training set
 With our training set defined  how do we used it?
 Take training set
 Pass into a learning algorithm
 Algorithm outputs a function (denoted h ) (h = hypothesis)
 This function takes an input (e.g. size of new house)
 Tries to output the estimated value of Y
 How do we represent hypothesis h ?
 Going to present h as;
 h_{θ}(x) = θ_{0} + θ_{1}x
 What does this mean?
 Means Y is a linear function of x!
 θ_{i} are parameters
 θ_{0} is zero condition
 θ_{1} is gradient
 This kind of function is a linear regression with one variable
 Also called univariate linear regression
 So in summary
 A hypothesis takes in some variable
 Uses parameters determined by a learning system
 Outputs a prediction based on that input
Linear regression  implementation (cost function) A cost function lets us figure out how to fit the best straight line to our data
 Choosing values for θ_{i} (parameters)
 Different values give you different functions
 If θ_{0} is 1.5 and θ_{1} is 0 then we get straight line parallel with X along 1.5 @ y
 If θ_{1} is > 0 then we get a positive slope
 Based on our training set we want to generate parameters which make the straight line
 Chosen these parameters so h_{θ}(x) is close to y for our training examples
 Basically, uses xs in training set with h_{θ}(x) to give output which is as close to the actual y value as possible
 Think of h_{θ}(x) as a "y imitator"  it tries to convert the x into y, and considering we already have y we can evaluate how well h_{θ}(x) does this
 To formalize this;
 We want to want to solve a minimization problem
 Minimize (h_{θ}(x)  y)^{2 }
 i.e. minimize the difference between h(x) and y for each/any/every example
 Sum this over the training set
 Minimize squared different between predicted house price and actual house price
 1/2m
 1/m  means we determine the average
 1/2m the 2 makes the math a bit easier, and doesn't change the constants we determine at all (i.e. half the smallest value is still the smallest value!)
 Minimizing θ_{0}/θ_{1} means we get the values of θ_{0} and θ_{1} which find on average the minimal deviation of x from y when we use those parameters in our hypothesis function
 More cleanly, this is a cost function
 And we want to minimize this cost function
 Our cost function is (because of the summaration term) inherently looking at ALL the data in the training set at any time
 So to recap
 Hypothesis  is like your prediction machine, throw in an x value, get a putative y value
 Cost  is a way to, using your training data, determine values for your θ values which make the hypothesis as accurate as possible
 This cost function is also called the squared error cost function
 This cost function is reasonable choice for most regression functions
 Probably most commonly used function
 In case J(θ_{0},θ_{1}) is a bit abstract, going into what it does, why it works and how we use it in the coming sections
Cost function  a deeper look  Lets consider some intuition about the cost function and why we want to use it
 The cost function determines parameters
 The value associated with the parameters determines how your hypothesis behaves, with different values generate different
 Simplified hypothesis
 Cost function and goal here are very similar to when we have θ_{0}, but with a simpler parameter
 Simplified hypothesis makes visualizing cost function J() a bit easier
 So hypothesis pass through 0,0
 Two key functions we want to understand
 h_{θ}(x)
 Hypothesis is a function of x  function of what the size of the house is
 J(θ_{1})
 Is a function of the parameter of θ_{1}
 So for example
 Plot
 θ_{1} vs J(θ_{1})
 Data
 1)
 2)
 θ_{1} = 0.5
 J(θ_{1}) = ~0.58
 3)
 If we compute a range of values plot
 J(θ_{1}) vs θ_{1} we get a polynomial (looks like a quadratic)
 The optimization objective for the learning algorithm is find the value of θ_{1} which minimizes J(θ_{1})
 So, here θ_{1} = 1 is the best value for θ_{1}
A deeper insight into the cost function  simplified cost function  Assume you're familiar with contour plots or contour figures
 Using same cost function, hypothesis and goal as previously
 It's OK to skip parts of this section if you don't understand contour plots
 Using our original complex hypothesis with two parameters,
 Example,
 Say
 Previously we plotted our cost function by plotting
 Now we have two parameters
 Plot becomes a bit more complicated
 Generates a 3D surface plot where axis are
 X = θ_{1}
 Z = θ_{0}
 Y = J(θ_{0},θ_{1})
 We can see that the height (y) indicates the value of the cost function, so find where y is at a minimum
 Instead of a surface plot we can use a contour figures/plots
 Set of ellipses in different colors
 Each color is the same value of J(θ_{0}, θ_{1}), but obviously plot to different locations because θ_{1} and θ_{0} will vary
 Imagine a bowl shape function coming out of the screen so the middle is the concentric circles
 Each point (like the red one above) represents a pair of parameter values for Ɵ0 and Ɵ1
 Our example here put the values at
 θ_{0} = ~800
 θ_{1} = ~0.15
 Not a good fit
 i.e. these parameters give a value on our contour plot far from the center
 If we have
 θ_{0} = ~360
 θ_{1} = 0
 This gives a better hypothesis, but still not great  not in the center of the countour plot
 Finally we find the minimum, which gives the best hypothesis
 Doing this by eye/hand is a pain in the ass
 What we really want is an efficient algorithm fro finding the minimum for θ_{0} and θ_{1}
Gradient descent algorithm  Minimize cost function J
 Gradient descent
 Used all over machine learning for minimization
 Start by looking at a general J() function
 Problem
 We have J(θ_{0}, θ_{1})
 We want to get min J(θ_{0}, θ_{1})
 Gradient descent applies to more general functions
 J(θ_{0}, θ_{1}, θ_{2} .... θ_{n})
 min J(θ_{0}, θ_{1}, θ_{2} .... θ_{n})
How does it work? Start with initial guesses
 Start at 0,0 (or any other value)
 Keeping changing θ_{0} and θ_{1} a little bit to try and reduce J(θ_{0},θ_{1})
 Each time you change the parameters, you select the gradient which reduces J(θ_{0},θ_{1}) the most possible
 Repeat
 Do so until you converge to a local minimum
 Has an interesting property
 Where you start can determine which minimum you end up
 Here we can see one initialization point led to one local minimum
 The other led to a different one
A more formal definition Do the following until convergence
 What does this all mean?
 Update θ_{j} by setting it to (θ_{j}  α) times the partial derivative of the cost function with respect to θ_{j}
 Notation
 :=
 Denotes assignment
 NB a = b is a truth assertion
 α (alpha)
 Is a number called the learning rate
 Controls how big a step you take
 If α is big have an aggressive gradient descent
 If α is small take tiny steps
 Derivative term
 Not going to talk about it now, derive it later
 There is a subtly about how this gradient descent algorithm is implemented
 Do this for θ_{0} and θ_{1}
 For j = 0 and j = 1 means we simultaneously update both
 How do we do this?
 Compute the right hand side for both θ_{0 }and θ_{1}
 Then, update θ_{0 }and θ_{1} at the same time
 We show this graphically below
 If you implement the nonsimultaneous update it's not gradient descent, and will behave weirdly
 But it might look sort of right  so it's important to remember this!
Understanding the algorithm To understand gradient descent, we'll return to a simpler function where we minimize one parameter to help explain the algorithm in more detail
 min θ_{1} J(θ_{1}) where θ_{1} is a real number
 Two key terms in the algorithm
 Notation nuances
 Partial derivative vs. derivative
 Use partial derivative when we have multiple variables but only derive with respect to one
 Use derivative when we are deriving with respect to all the variables
 Derivative term
 Derivative says
 Lets take the tangent at the point and look at the slope of the line
 So moving towards the minimum (down) will generate a negative derivative, alpha is always positive, so will update j(θ_{1}) to a smaller value
 Similarly, if we're moving up a slope we make j(θ_{1}) a bigger numbers
 Alpha term (α)
 What happens if alpha is too small or too large
 Too small
 Take baby steps
 Takes too long
 Too large
 Can overshoot the minimum and fail to converge
 When you get to a local minimum
 Gradient of tangent/derivative is 0
 So derivative term = 0
 alpha * 0 = 0
 So θ_{1} = θ_{1} 0
 So θ_{1} remains the same
 As you approach the global minimum the derivative term gets smaller, so your update gets smaller, even with alpha is fixed
 Means as the algorithm runs you take smaller steps as you approach the minimum
 So no need to change alpha over time
Linear regression with gradient descent  Apply gradient descent to minimize the squared error cost function J(θ_{0}, θ_{1})
 Now we have a partial derivative
 So here we're just expanding out the first expression
 J(θ_{0}, θ_{1}) = 1/2m....
 h_{θ}(x) = θ_{0} + θ_{1}*x
 So we need to determine the derivative for each parameter  i.e.
 Figure out what this partial derivative is for the θ_{0} and θ_{1} case
 When we derive this expression in terms of j = 0 and j = 1 we get the following
 To check this you need to know multivariate calculus
 So we can plug these values back into the gradient descent algorithm
 How does it work
 Risk of meeting different local optimum
 The linear regression cost function is always a convex function  always has a single minimum
 Bowl shaped
 One global optima
 So gradient descent will always converge to global optima
 In action
 End up at a global minimum
 This is actually Batch Gradient Descent
 Refers to the fact that over each step you look at all the training data
 Each step compute over m training examples
 Sometimes nonbatch versions exist, which look at small data subsets
 We'll look at other forms of gradient descent (to use when m is too large) later in the course
 There exists a numerical solution for finding a solution for a minimum function
 Normal equations method
 Gradient descent scales better to large data sets though
 Used in lots of contexts and machine learning
What's next  important extensionsTwo extension to the algorithm 1) Normal equation for numeric solution
 To solve the minimization problem we can solve it [ min J(θ_{0}, θ_{1}) ] exactly using a numeric method which avoids the iterative approach used by gradient descent
 Normal equations method
 Has advantages and disadvantages
 Advantage
 No longer an alpha term
 Can be much faster for some problems
 Disadvantage
 We discuss the normal equation in the linear regression with multiple features section
 2) We can learn with a larger number of features
 So may have other parameters which contribute towards a prize
 e.g. with houses
 Size
 Age
 Number bedrooms
 Number floors
 x1, x2, x3, x4
 With multiple features becomes hard to plot
 Can't really plot in more than 3 dimensions
 Notation becomes more complicated too
 Best way to get around with this is the notation of linear algebra
 Gives notation and set of things you can do with matrices and vectors
 e.g. Matrix
 We see here this matrix shows us
 Size
 Number of bedrooms
 Number floors
 Age of home
 All in one variable
 Block of numbers, take all data organized into one big block
 Vector
 Shown as y
 Shows us the prices
 Need linear algebra for more complex linear regression models
 Linear algebra is good for making computationally efficient models (as seen later too)
 Provide a good way to work with large sets of data sets
 Typically vectorization of a problem is a common optimization technique

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