Week1:
Machine Learning:
- 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.
- Supervised Learning:We already know what our correct output should look like.
- Regression:Try to map input variables to some continuous function.
- Classification:Try to map input variables into discrete categories.
- Unsupervised Learning:We only have little or no idea what our results should look like.
- Clustering:Find a way to automatically group data into groups that are somehow similar or related by different variables.
- Non-clustering:Find structure in a chaotic environment,like the "Cocktail Party Algorithm".
Model Representation:
- x(i):Input features
- y(i):Target variable
- (x(i),y(i)):Training example
- (x(i),y(i));i=1,...,m:Training set
- m:Number of training examples
- h(x):Hypothesis,θ0+θ1x1
Cost Function:
- This takes an average difference of all the results of the hypothesis with inputs from x's and the actual output y's.
- Algorithm:
(The mean is halved 1/2 as a convenience for the computation of the gradient descent, as the derivative term of the square function will cancel out the 1/2 term.) - We use contour plot to show how to minimize the cost function.
Gradient Descent:
- Help us to estimate the parameters in the hypothesis function.
- Algorithm:
(repeat until convergence) - j=0,1:Feature index numbe
- α:Learning rate or the size of each step.If α is too small,gradient descent can be slow.If α is too large,gradient descent can overshoot the minimum.
- Partial Derivative of J:Direction of each step
- At each iteration j, one should simultaneously update all of the parameters.
Gradient Descent For Linear Regression:
- Algorithm:
- This method looks at every example in the entire training set on every step, and is calledbatch gradient descent.
Linear Algebra:
- I have learned liner algebra in my college so I will skip this part in my note.
Week2:
Mutiple Features:
- n:number of features
- x(i):input of ith training example
- x(i)j:value of feature j in ith training example
- hθ(x):θ0x0+θ1x1+θ2x2+θ3x3+⋯+θnxn=
(assume x0 = 1)
Gradient Descent for Multiple Variables:
- Algorithm:
- Feature Scaling:
- Feature Scaling:Dividing the input values by the range (max - min) of the input variable.Get every feature into approximately a -1 <= xi <= 1 range.
- Mean Normalization:Subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero.
-
Where μi is the average of all the values for feature i and si is the range of values (max - min), or si is the standard deviation.
- Learning Rate:Make a plot with number of iterations on the x-axis. and J(θ) on the y-axis.If J(θ) ever increases, then you probably need to decrease α.It has been proven that if learning rate α is sufficiently small, then J(θ) will decrease on every iteration.To choose α,try 0.001,0.003,0.01......
- Features and Polynomial Regression:We can improve our features and the form of our hypothesis function in a couple different ways
- We can combine multiple features into one.We can get a new feature x3 by taking x1 * x2
- We can change the behavior or curve of our hypothesis function by making it a quadratic, cubic or square root function (or any other form).
- if you choose your features this way then feature scaling becomes very important.
Normal Equation:
- Formula:
- Example:
- There is no need to do feature scaling with the normal equation.
- If (X^TX) is non-invertibale:
- Delete redundant features such as x1 = size in feet^2 and x2 = size in m^2.
- Delete features to make sure that m > n or use regularization.
Octave:
- The classification problem is just like the regression problem, except that the values we now want to predict take on only a small number of discrete values.
- x(i):Feature
- y(i):Label for the tranning example
Logistic Regression:
- We change the form for our hypotheses to satisfy 0 <= h(x) =1 by pluggin θ^Tx into the Logistic Function.
- Formula:
- Decision Boundary:The line that separates the area where y = 0 and where y = 1.It is created by hypothesis function(θ^Tx=0).
- Cost Function:
We can compress our cost function's two conditional cases into one case:
- Gradient Descent: This algorithm is identical to the one we used in linear regression.But the h(x) is changed.
Optimization Algorithms:
- Conjugate gradient
- BFGS
- L-BGFS
- We can write codes below to use Octave's "fminunc()"
Multiclass Classification:
- Train a logistic regression classifier hθ(x) for each class to predict the probability that  y = i . To make a prediction on a new x, pick the class that maximizes hθ(x)
Overfitting:
- Even though the fitted curve passes through the data perfectly, we would not expect this to be a very good predictor.
- Options to address overfitting:
- Reduce the number of features.
- Regularzation.
- Regularized Linear Regression:
- Cost Funcion:
(lambda is the regularization parameter.) - Gradient Descent:
- Normal Equation:
- Regularized Logistic Regression:
- Cost Function:
- Gradient Descent:
Week4:
Neural Network:Representation:
- If we had one hidden layer, it would look like:
- The values for each of the "activation" nodes:
- Each layer gets its own matrix of weights:
(The '+1' comes from the 'bias nodes',the output nodes will not include the bias nodes while the inputs will.) - Vectorized:
- We can set different theta matrix to construct fundamental options by using a small neural network.
- We can construct more complex options by using hidden layers.
- Multiclass Classification:We use one-vs-all method and let hypothesis function return a vector of values.
Week 5:
Neural Network:Learning:
Cost Function:
- L:Total number of layers in the network
- Sl:Number of units (not counting bias unit) in layer l
- K:number of output units/classes
Backpropagation Algorithm:
- "Backpropagation" is neural-network terminology for minimizing our cost function.
- Algorithm:For t = 1 to m:
- We get
- Using code like this to unroll all the elements and put them into one long vector.
Using code like this to get back original matrices.
- Gradient Checking:We can approximate the derivative with respect to θj as follows:
- Training:
Week 6:
Applying Machine Learning:
Evaluating a Hypothesis:
- Set 70% of date to be the training set and the remainning 30% to be the test set.
- In order to choose the model of your hypothesis, we can test each degree of polynomial by using cross validation set.(20% training set,20% cross validation set,60% test set)
Bias vs. Variance:
- High bias is underfitting and high variance is overfitting.Ideally, we need to find a golden mean between these two.
- High Bias:
- High Variance:
- In order to choose the model and the regularization term λ, we need to:
- If a learning algorithm is suffering from high bias, getting more training data will not help much.
- If a learning algorithm is suffering from high variance, getting more training data is likely to help.
- A neural neural network with fewer parameters is prone to underfitting. It is also computationally cheaper.
- A large neural network with more parameters is prone to overfitting. It is also computationally expensive.
Machine Learning System Desing:
- The recommended approach:
- Start with a simple algorithm, implement it quickly, and test it early on your cross validation data.
- Plot learning curves to decide if more data, more features, etc. are likely to help.
- Manually examine the errors on examples in the cross validation set and try to spot a trend where most of the errors were made.
- It is very important to get error results as a single, numerical value.
- Precision
Handling Skewed Data:
- Skewed Classes:The ratio of positive to negative examples is very close to one of two extremes.
-
(y = 1 in presence of rare class that we want to detect) - Precision Rate:TP / (TP + FP)
- Recall Rate:TP / (TP + FN)
- F1 Score:(2 * P * R) / (P + R)
Week 7:
Support Vector Machines:
Optimization Objective:
- Because constant doesn't change value of the theta that achieves the miinmum,so we multiplying objective function in logistic regression by M.
- We can both use (A + λB) or (CA + B) to control the relative.
- A support vector machine just makes a prediction of y being equal to one or zero, directly. So the hypothesis will predict one
Large Margin Intuition:
- The SVM decision boundary will become like this:
- The black line gives SVM a robustness because it has a large margin:
Kernels:
- Given (xi,yi),we choose li = xi as landmarks,then let fi = sim(x,li).
- We compute new features depending on proximity to landmarks.So our function become theta0 + theta1*f1 + theta2*f2......
- Gaussian Kernels:
- C and Sigma:
- Do perform feature scaling before using the Gaussian kernel.
- Linear kernel:meanning no kernel.
Week8:
Unsupervised Learning:
Clustering:
- We give unlabeled training set to an algorithm and we ask the algorithm find some structure in the data for us.
- K-meas Algorithm:
- Cost Function:
- Random Initialization:Randomly pick k training examples and set Mu1 of MuK equal to these k examples.
- Elbow Method:
- Better way to choose the number of clusters is to ask, for what purpose are you running K-means.
Dimensionality Reduction:
- Reason:Data compression or speed up our learning algorithm.
-
- Visualization:We can use dimensionality reduction to reduce data from high dimensions down to 2 or 3 dimensions,so that we can plot it and understand our data better.
Principal Component Analysis:
- PCA:Find a lower dimensional surface onto which to project the data, so as to minimize the square distance between each point and the location of where it gets projected.
- Reduce from 2D to 1D:Find a vector onto which to project the data to minimize the projection error.
- Reduce from nD to kD:Find k vectors onto which to project the data to minimize the projection error.
- Data preprocessing:Feature scaling/Mean normalization
- Algorithm:
- If we want to reduce the data from n dimensions down to k dimensions, we need to do is take the first k vectors from U(n * n) as Ureduce(n * k).
- z = Ureduce' * x.
- Reconstruction from Compressed Representation:Xapprox = Ureduce * z.
-
- Applying:
(Only if your algorithm doesn't do what you want then implement PCA)
Week 9:
Anomaly Detection:
Density Estimation:
- We build a model of the probability of x,if p of x-test is less than some epsilon then we flag this as an anomaly.
- Gaussian Distribution(Normal Distribution):
,
- Parameter Estimation:
- Algorithm:
- Evaluation:Assume we have some labled data of anomalous and nonanomalous examples.Using training set(unlabled,assume normal examples),cross validation set and test set.
- Anomaly Detection vs. Supervised Learning:
- Non-gaussian Features:Let xNew = log(x)(logarithmic normal distribution),or xNew = x^(0.1)
- Choose Features:Choose features that migth take on unusually large or small values in the event of an anomaly
Multivariate Gaussian Distribution:
Recommender Systems:
- n.u = number of users
- n.m = number of moives
- r(i,j) = 1 if user j have rated movie i
- y(i,j) = rating given by user j to movie i(only if r(i,j) = 1)
- theta(j) = parameter vector for user j
- x(i) = feature vector for movie i
Content Based Recommendations:
- We assume we have features for different movies.
- For each user j,learn a parameter.Predict user j as rating movie i with
stars. - Optimization Objective:
- Gradient Descent:
Collaborative Filtering:
- We assume that each of our users has told us how much they like the romantic movies and how much they like action packed movies.
- Optimization Algorithm:
- Given x and movie ratings can estimate theta.
- Given theta and movie ratings can estimate x.
- Optimization Objective:
- Mean Normalization:Compute the average rating that each movie obtained and subtract off the meaning rating.So the rating of movie become + average rating.
Week 10:
Large Scale Machine Learning:
Stochastic Gradient Descent:
- Algorithm:
- Randomly shuffle the data set.
- For i = 1...m:
- SGD will only try to fit one training example at a time. This way we can make progress in gradient descent without having to scan all m training examples first.
- We will usually take 1-10 passes through data set to get near the global minimum.
- Convergence:Plot the average cost of the hypothesis applied to every 1000 or so training examples. We can compute and save these costs during the gradient descent iterations.
- One strategy for trying to actually converge at the global minimum is to slowly decrease α over time.
Mini-Batch Gradient Descent:
- Use b examples in each iteration.(b = mini-batch size)
- Algorithm:
- The advantage is that we can use vectorized implementations over the b examples.
Online Learning:
- With a continuous stream of users to a website, we can run an endless loop that gets (x,y), where we collect some user actions for the features in x to predict some behavior y.
- You can update θ for each individual (x,y) pair as you collect them. This way, you can adapt to new pools of users, since you are continuously updating theta.
Map Reduce and Data Parallelism:
- Many learning algorithms can be expressed as computing sums of functions over the training set.
- We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines so that we can train our algorithm in parallel.
Week 11:
Photo OCR:
- Pipeline:
- Text detection
- Character segmentation
- Character classification
- Using sliding windows and expansion to text detection and character segmentation
- Ceiling Analysis
Artificial Data Synthesis:
- Creating new data from scratch(using the ramming funds as an example)
- Taking existing label examples and introducing distortions to it, to sort of create extra label examples.
