# INTRODUCTION TO MACHINE LEARNING. Machine Learning: What s The Challenge?

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1 INTRODUCTION TO MACHINE LEARNING Machine Learning: What s The Challenge?

2 Goals of the course Identify a machine learning problem Use basic machine learning techniques Think about your data/results

3 What is Machine Learning? Construct/use algorithms that learn from data More information Higher performance Previous solutions Experience

4 Example Label squares: size and edge color Earlier observations (labeled by humans): Task for computer = label unseen square:? Result: right or wrong!

5 Input Knowledge Features Label In example: pre-labeled squares size edge color small dotted green Observations big striped yellow In R - use data.frame() medium normal green > squares <- data.frame( size = c("small", "big", "medium"), edge = c("dotted", "striped", "normal"), color = c("green", "yellow", "green"))

6 Data Frame Functions > dim(squares) #Observations, #Features > str(squares) Structured Overview > summary(squares) Distribution Measures

7 Formulation INPUT FUNCTION OUTPUT ESTIMATED FUNCTION COLOR

8 ML: What It Is Not Determining most occurring color Calculating average size } NOT Machine Learning Goal: Building models for prediction!

9 Regression Regression INPUT: Weight OUTPUT: Height Estimated function: Weight Height

10 More Applications! Shopping basket analysis Movie recommendation systems Decision making for self-driving cars and many more!

11 INTRODUCTION TO MACHINE LEARNING Let s practice!

12 INTRODUCTION TO MACHINE LEARNING Classification Regression Clustering

13 Common ML Problems Classification Regression Clustering

14 Classification Problem Goal: predict category of new observation Estimate Earlier Observations CLASSIFIER CLASSIFIER Unseen Data Class

15 Classification Applications Medical Diagnosis Sick and Not Sick Animal Recognition Dog, Cat and Horse Important: Qualitative Output Predefined Classes

16 Regression PREDICTORS REGRESSION FUNCTION RESPONSE Relationship: Height - Weight? Linear? Predict: Weight Height

17 Regression Model Fitting a linear function Predictor: Response: Coefficients: Estimate on previous input-output > lm(response ~ predictor)

18 Regression Applications Payments Credit Scores Time Subscriptions Grades Landing a Job Quantitative Output Previous input-output observations

19 Clustering Clustering: grouping objects in clusters Similar within cluster Dissimilar between clusters Example: Grouping similar animal photos No labels No right or wrong Plenty possible clusterings

20 k-means Cluster data in k clusters! y y x x

21 INTRODUCTION TO MACHINE LEARNING Let s Practice

22 INTRODUCTION TO MACHINE LEARNING Supervised vs. Unsupervised

23 Machine Learning Tasks Classification Regression quite similar Clustering

24 Supervised Learning Find: function f which can be used to assign a class or value to unseen observations. Given: a set of labeled observations Supervised Learning

25 Unsupervised Learning Labeling can be tedious, often done by humans Some techniques don t require labeled data Unsupervised Learning Clustering: find groups observation that are similar Does not require labeled observations

26 Performance of the model Supervised Learning Compare real labels with predicted labels Predictions should be similar to real labels Unsupervised Learning No real labels to compare Techniques will be explained in this course

27 Semi-Supervised Learning A lot of unlabeled observations A few labeled Group similar observations using clustering Use clustering information and classes of labeled observations to assign a class to unlabelled observations More labeled observations for supervised learning

28 INTRODUCTION TO MACHINE LEARNING Let s practice!

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