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Summary Machine Learning
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Summary of all the lectures of Machine Learning. It contains all the relevant material needed for the final exam.
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Summary Machine LearningLecture 1 – Introduction to Machine Learning
What is machine learning (ML) about?
ML is about automation of problem solving.
It is the study of computer algorithms that improve automatically through experience.
Involves becoming better at a task T based on some experience E with respect to some
performance measure P
Examples: spam detection, movie recommendation, speech recognition, credit risk analysis,
autonomous driving and medical diagnosis.
What does it involve?
ML may involve a notion of generalization. Is it safe to assume that current observations can
be generalized to future observations?
ML should be generalizable – should perform on unseen data / representative for real world
domain.
Labeled data, objective, optimization algorithm (models), features/representations (columns),
and assumptions are some critical components.
Different types of learning
Supervised learning: annotated/labelled dataset / ground truth
o Classification: discrete variable - predicting the price of a house
o Regression: continuous variable - spam detection
Unsupervised learning: unlabeled dataset
o Clustering, association mining - customer segmentation, recommendation
Semi supervised learning (only a portion of the data is labeled) - text classification
Reinforcement learning: based on rewarding desired behaviors and/or punishing undesired
ones. (involves a feedback loop) - self driving car
Example - SPAM versus non-Spam
Binary classification problem
Learning process
Find examples of SPAM and non-SPAM
Come up with a learning algorithm
A learning algorithm infers rules from examples: If (A or B or C) and not D, then SPAM
These rules can then be applied to new data (emails)
Learning algorithms
, See several different learning algorithms
Implement simple 2-3 simple ones from scratch in Python
Learn about Python libraries for ML (scikit-learn)
How to apply them to real-world problems
Machine Learning – Examples
Recognize handwritten numbers and letters
Recognize faces in photos
Determine whether text expresses positive, negative or no opinion
Guess person’s age based on a sample of writing
Flag suspicious credit-card transactions
Recommend books and movies to users based on their own and others’ purchase history
Recognize and label mentions of people’s or organization names in text
Types of learning problems: Regression
Response: a (real) number
Predict person’s age, predict price of a stock, predict student’s score on exam
In regression, the response variable is predicted using a set of predictors that are believed to
have an influence on the response variable.
Types of learning problems: Binary classification
Response: Yes/No answer
Detect SPAM
Predict polarity of product review: positive vs negative
Types of learning problems: Multiclass classification
Response: one of a finite set of options
Classify newspaper article as
o politics, sports, science, technology, health, finance
Detect species based on photo
o Passer domesticus, Calidris alba, Streptopelia decaocto, Corvus corax, …
Types of learning problems: Multilabel classification
Response: a finite set of Yes/No answers
Assign songs to one or more genres
o rock, pop, metal
o hip-hop, rap
o jazz, blues
o rock, punk
Types of learning problems: Autonomous behavior
Input: measurements from sensors – camera, microphone, radar, accelerometer,. . .
Response: instructions for actuators – steering, accelerator, brake,
How well is the algorithm learning?
Evaluation: Choose a baseline, choose a metric, compare!
, different tasks, different metrics
Predicting age – Regression
Mean absolute error – the average (absolute) difference between true value and predicted
value (yn true value (ground truth), ^y n predicted value) - fails to punish large errors in
prediction as all errors are treated equally.
Mean squared error – the average square of the difference between true value and predicted
value - more sensitive to outliers as the square amplifies the impact of large deviations.
Predicting spam - Classification
Drawback: does not work well on imbalanced data. If the data is imbalanced the accuracy
will naturally be high.
Classification
Wrong classification
False positive (FP) – Flagged as SPAM, but not non-SPAM
False negative (FN) – Not flagged, but is SPAM
False positives are a bigger issue for this problem! In the medical field this is the other way
around (False negative are the bigger issue). Minimizing one of the two depends thus on the
problem at hand.
Correct classification
True positive (TP): Spam classified as spam
True negative (TN): Not-spam classified as not-spam
Summarized in a confusion matrix (image on the right)
Confusion matrix can be appended with more decision
classes.
Precision and Recall
Metrics which focus on one kind of mistake. These are better for imbalanced data (together
with F1-score.
Precision (positive predictive value (PPV)) – what fraction of flagged emails
were real SPAMs?
Recall (sensitivity, hit rate, or true positive rate (TPR)) – what fraction of
real SPAMs were flagged?
Specificity, selectivity or true negative rate (TNR) (usage not common)
Fβ-score
F1 – score (F-measure): harmonic mean between precision and recall a kind
of average
, Parameter β quantifies how much more we care about recall than precision,
when it is greater than 1, that means, recall is weighted more, when it is
smaller than 1, that means precision is weighted more.
Macro-average
Precision and recall are usually calculated per decision class. Micro and
macro average are ways to aggregate the measures.
Precision true positives over labeled positives; Recall, true positives over
actual positives. ((1), (2), (3), (4),(5)) represent the five data points.)
Compute precision and recall per-class, and average: ex:
Rare classes have the same impact as frequent classes (not ideal).
Macro F1-Score is the harmonic mean of Macro-Precision and Macro-Recall.
Micro-average
Micro averaging treats the entire set of data as an aggregate result, and calculates 1 metric
rather than k metrics that get averaged together.
In micro averaging, we calculate one aggregate result for the entire data set (for precision and
recall and use these micro averaged precision and recall for the micro averaged F1).
Micro-Average Precision and Recall are just the same values when there is one label, so is the
Micro Average F1-Score, and the accuracy.
How to find ^f ( x ) : A solution workflow
Best outcome we can hope for: ^f ( x ) = f (x) for all x. Ideally, we would like ^f ( x ) such that a
loss between f(x) and ^f ( x ) is minimized, i.e., L( ^f ( x ) , f (x)) is small.
The cost (average loss plus possibly a regularization term) in case of regression can be MSE
or MAE, computed over all values of x
Problem 1 We do not have all values of x and f (x) (might not represent the whole population)
Problem 2 We do not know how f (x) looks like (distribution)
Compute loss on the data we have (empirical risk minimization) for MAE:
How to find ^f ( x )
If ^f ( x ) = θx + c we assume a linear relationship
For a more complex relationship, a polynomial function can also be used
Choose a power p ^f ( x ) = c + θ1x + θ2x2 + . . . θpxp Higher p implies higher degree of
freedom/flexibility (and more fitted to the data -> risk of overfitting)
Changing p: overfitting / underfitting
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