machine learning- key concepts

Machine learningApplications, types and key concepts

● What is machine learning?

● Applications

● Types

● Terminology

● Key concepts

Outline

Next classes1. Key concepts

2. A tour of machine learning (linear algebra, probability theory, calculus)

3. Machine learning pipelines (pre-processing, model training, and evaluation in Python and Scala)

4. Machine learning case studies (Python and Scala examples)

a. Sentiment analysis (Natural Language Processing, NLTK)

b. Spam classifier

c. Stock price prediction (regression)

d. Image recognition, deep learning (TensorFlow, keras)

e. Recommendation engine

5. Machine learning at scale (algorithms, linear algebra, probability, Spark MLLib, Vowpal Wabbit, scikit-learn)

Next classes

Key concepts Tour Pipelines Case studies Scale

Concepts ০০০ ০ ০০ ০০ ০০০

Code ০ ০০ ০০০ ০০০ ০০০

Math/stats ০ ০০০ ০ ০ ০০০

What is machine learning?● Learn from data (past experiences)

● Generalize (find the signal/pattern)

● Predict, forward looking

● Observational data

Relationship to data science and deep learning

What is machine learning?

Data Science

ML

DL

Applications● Autonomous cars

● Siri

● Facial recognition

● People who bought this also bought...

● Spam filters

● Targeted advertising

● ...

Types of machine learning● Supervised learning

○ Classification

○ Regression

● Unsupervised learning

● Reinforcement learning

Types of machine learning

Supervised Unsupervised Reinforcement learning

Cancer diagnosisStock market predictionCustomer churnRecommendation engineAnomaly detection

Dimensionality reductionClusteringPageRankAnomaly detection

Self-driving carsAlphaGo

(Linear) Regression● Predict a continuous variable (e.g. price)

● Y=mx+b

● Ordinary Least Squares

● Analytical solution

● Geometric model

(Linear) Regression● Can use multiple variables

(multi-variate regression)

● Relationships are not always linear

(Linear) Regression example● Boston housing dataset

● Median value of houses (MV)

vs. average # rooms (RM)

from sklearn.linear_model import LinearRegressionmodel = LinearRegression()x, y = housing[['RM']], housing['MV']model.fit(x, y)model.score(x, y)

R2=0.48

(Linear) Regression example● Boston housing dataset

● Median value of houses (MV)

vs. average # rooms (RM),

and industrial zoning proportions (INDUS)

from sklearn.linear_model import LinearRegressionmodel = LinearRegression()x, y = housing[['RM', ‘INDUS’]], housing['MV']model.fit(x, y)model.score(x, y)

R2=0.53

(Linear) Regression example● Intuition breaks down in high-dimensions (>3)

● Interpretability goes down

● Real-world data is usually non-linear

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Terminology● Feature (a.k.a. input, variable, predictor, explanatory, independent variable)

● Output (a.k.a. target, label, class, dependent variable)

● Training instance (aka observation, row)

● Training dataset

● Training (a.k.a. learning, modeling, fitting)

● Model validation and testing

RM INDUS ZN ... MV

6.575 2.31 18.0 ... 24.0

6.421 7.07 0.0 ... 21.6

... ... ... ... ...

Statistical learning● The true underlying function is not known

● Usually can’t observe all features (e.g. policy impact, global trends, etc.)

● Most interesting phenomenon are neither deterministic, nor stationary

● No guarantee that a set of variables is predictive of the outcome

Machine learning territory100% deterministicF=ma

100% stochasticCoin flip

Classification● Target variable, qualitative, classes

● Binary classification

Cancer patientPositive class (class of interest)

Healthy patientNegative class

Classification● Linear vs. non-linear decision boundaries

● Model complexity, training time, and latency

Bias

Cancer patientPositive class (class of interest)

Healthy patientNegative class

Bias

Cancer patientPositive class (class of interest)

Healthy patientNegative class

Bias

Variance/overfit● Learning the wrong things, memorizing

● Modeling the noise and not the signal

○ Model 1- if GPA > 3.8 and hours studied>5 then passed

○ Model 2- if student ID != 2 then passed

○ New record: StudentID = 4, Hours Studied = 5.5, GPA = 3.82, passed?

Student ID Hours studied GPA ... Passed

1 10 4.00 Yes

2 0 2.71 No

3 6 3.95 Yes

Bias/variance

Guarding against overfitting● Split into train, validation and test

● Cross-validation

SummaryIncreasing model complexity generally:

● Increases model fit

● Decreases interpretability

● Increases chance of overfitting

● Increases training time

● Increases model latency

Remember that...

Next class: a tour of machine learning

Preparation for next class1- Test your understanding: http://bit.ly/mlseries1

2- Check this out: Visual intro to ML

Want to pursue machine learning more seriously?

● Read A few useful things to know about machine learning

● Theory and intuition, Python Machine Learning book

● Hands-on experience, Kaggle (start with titanic)

● Elements of statistical learning (advanced)