CS260 Machine Learning Algorithms: Course Overview, Study notes of Machine Learning

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Course Overview

Professor Ameet Talwalkar

Outline

1 Overview of machine learning

What is machine learning?

2 About this Course

Example: detect patterns

How the temperature has been changing in the last 140 years?

Patterns

We see repeated periods of fluctuation

General trend is that temperatures are rising

How do we describe the pattern?

Predicting future

What is temperature of 2017?

This particular polynomial model is not exactly accurate for that

specific year, but it is pretty close

What we have learned from this example?

Key ingredients in the machine learning task

What we have learned from this example?

Key ingredients in the machine learning task

Data: collected from past observations (training data)

Modeling: devised to capture the patterns in the data

I The model does not have to be true — as long as it is close, it is useful

I We should tolerate randomness and mistakes — many interesting

things are stochastic by nature.

What we have learned from this example?

Key ingredients in the machine learning task

Data: collected from past observations (training data)

Modeling: devised to capture the patterns in the data

I The model does not have to be true — as long as it is close, it is useful

I We should tolerate randomness and mistakes — many interesting

things are stochastic by nature.

Prediction: apply the model to forecast what is going to happen in

future

Huge success 20 years ago

Recognizing handwritten zipcodes and checks (AT&T Labs, circa

late 1990s)

true class = 7 true class = 2 true class = 1 true class = 0 true class = 4 true class = 1 true class = 4 true class = 9 true class = 5

More modern ones, in your social life

Recognizing your friends on Facebook

Why is machine learning so popular?

Why is machine learning so popular?

Data

I Flood of data from various sensors leads to several high-impact

applications

I e.g., cell phones, internet applications, scientific studies

Computing

I Powerful and cheaply available computing resources enables efficient

storage / processing / analysis of this data

I e.g., cloud computing, GPUs, cell phones

What is in machine learning?

Different flavors of learning problems

Supervised learning: make prediction given labeled training

observations, e.g., Spam detection, Iris

What is in machine learning?

Different flavors of learning problems

Supervised learning: make prediction given labeled training

observations, e.g., Spam detection, Iris

Unsupervised learning: Discover hidden and latent patterns in data;

data exploration, e.g., topic modelling in text data