Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the...
-
Upload
caren-randall -
Category
Documents
-
view
235 -
download
0
Transcript of Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the...
![Page 1: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/1.jpg)
1
Machine Learning Overview
Tamara Berg
CS 560 Artificial Intelligence
Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer, Rob Pless, Killian Weinberger, Deva Ramanan
![Page 2: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/2.jpg)
Announcements
• HW3 due Oct 29, 11:59pm
![Page 3: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/3.jpg)
Machine learning
Image source: https://www.coursera.org/course/ml
![Page 4: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/4.jpg)
Machine learning
• Definition– Getting a computer to do well on a task
without explicitly programming it– Improving performance on a task based on
experience
![Page 5: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/5.jpg)
Big Data!
![Page 6: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/6.jpg)
What is machine learning?
• Computer programs that can learn from data
• Two key components– Representation: how should we represent the data?– Generalization: the system should generalize from its
past experience (observed data items) to perform well on unseen data items.
![Page 7: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/7.jpg)
Types of ML algorithms
• Unsupervised– Algorithms operate on unlabeled examples
• Supervised– Algorithms operate on labeled examples
• Semi/Partially-supervised– Algorithms combine both labeled and unlabeled
examples
![Page 8: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/8.jpg)
![Page 9: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/9.jpg)
Clustering
– The assignment of objects into groups (aka clusters) so that objects in the same cluster are more similar to each other than objects in different clusters.
– Clustering is a common technique for statistical data analysis, used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics.
![Page 10: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/10.jpg)
Euclidean distance, angle between data vectors, etc
![Page 11: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/11.jpg)
![Page 12: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/12.jpg)
K-means clustering
• Want to minimize sum of squared Euclidean distances between points xi and their nearest cluster centers mk
k
ki
ki mxMXDcluster
clusterinpoint
2)(),(
![Page 13: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/13.jpg)
![Page 14: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/14.jpg)
![Page 15: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/15.jpg)
![Page 16: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/16.jpg)
![Page 17: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/17.jpg)
![Page 18: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/18.jpg)
![Page 19: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/19.jpg)
![Page 20: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/20.jpg)
![Page 21: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/21.jpg)
![Page 22: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/22.jpg)
![Page 23: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/23.jpg)
![Page 24: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/24.jpg)
K-means
0 0.5 1 1.5 2 2.5 30
0.5
1
1.5
2
2.5
3
3.5
4
xx
x
x’s – indicate initialization for 3 cluster centers
Iterate until convergence:
1) Compute assignment of data points to cluster centers
2) Update cluster centers with mean of assigned points
![Page 25: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/25.jpg)
![Page 26: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/26.jpg)
![Page 27: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/27.jpg)
Flat vs Hierarchical Clustering
• Flat algorithms– Usually start with a random partitioning of docs into
groups– Refine iteratively– Main algorithm: k-means
• Hierarchical algorithms– Create a hierarchy– Bottom-up: agglomerative– Top-down: divisive
![Page 28: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/28.jpg)
Hierarchical clustering strategies
• Agglomerative clustering• Start with each data point in a separate cluster• At each iteration, merge two of the “closest” clusters
• Divisive clustering• Start with all data points grouped into a single cluster• At each iteration, split the “largest” cluster
![Page 29: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/29.jpg)
PProduces a hierarchy of clusterings
P
P
P
![Page 30: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/30.jpg)
P
![Page 31: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/31.jpg)
Divisive Clustering
• Top-down (instead of bottom-up as in Agglomerative Clustering)
• Start with all data points in one big cluster
• Then recursively split clusters
• Eventually each data point forms a cluster on its own.
![Page 32: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/32.jpg)
Flat or hierarchical clustering?
• For high efficiency, use flat clustering (e.g. k means)
• For deterministic results: hierarchical clustering
• When a hierarchical structure is desired: hierarchical algorithm
• Hierarchical clustering can also be applied if K cannot be predetermined (can start without knowing K)
![Page 33: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/33.jpg)
Clustering in Action – example from computer vision
![Page 34: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/34.jpg)
Recall: Bag of Words Representation
· Represent document as a “bag of words”
![Page 35: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/35.jpg)
Bag of features for images
· Represent images as a “bag of words”
![Page 36: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/36.jpg)
Bags of features for image classification
1. Extract features
![Page 37: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/37.jpg)
1. Extract features
2. Learn “visual vocabulary”
Bags of features for image classification
![Page 38: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/38.jpg)
1. Extract features
2. Learn “visual vocabulary”
3. Represent images by frequencies of “visual words”
Bags of features for image classification
![Page 39: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/39.jpg)
…
1. Feature extraction
![Page 40: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/40.jpg)
2. Learning the visual vocabulary
…
![Page 41: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/41.jpg)
2. Learning the visual vocabulary
Clustering
…
![Page 42: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/42.jpg)
2. Learning the visual vocabulary
Clustering
…Visual vocabulary
![Page 43: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/43.jpg)
Example visual vocabulary
Fei-Fei et al. 2005
![Page 44: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/44.jpg)
3. Image representation
…..
fre
que
ncy
Visual words
![Page 45: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/45.jpg)
Types of ML algorithms
• Unsupervised– Algorithms operate on unlabeled examples
• Supervised– Algorithms operate on labeled examples
• Semi/Partially-supervised– Algorithms combine both labeled and unlabeled
examples
![Page 46: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/46.jpg)
![Page 47: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/47.jpg)
![Page 48: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/48.jpg)
![Page 49: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/49.jpg)
![Page 50: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/50.jpg)
Example: Sentiment analysis
http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/
http://nlp.stanford.edu:8080/sentiment/rntnDemo.html
![Page 51: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/51.jpg)
Example: Image classification
apple
pear
tomato
cow
dog
horse
input desired output
![Page 53: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/53.jpg)
Example: Seismic data
Body wave magnitude
Sur
face
wav
e m
agni
tude
Nuclear explosions
Earthquakes
![Page 54: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/54.jpg)
![Page 55: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/55.jpg)
The basic classification framework
y = f(x)
• Learning: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the parameters of the prediction function f
• Inference: apply f to a never before seen test example x and output the predicted value y = f(x)
output classification function
input
![Page 56: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/56.jpg)
Naïve Bayes Classification
The class that maximizes:
![Page 57: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/57.jpg)
Example: Image classification
Car
Input: Image Representation Classifier (e.g. Naïve Bayes, Neural Net, etc
Output: Predicted label
![Page 58: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/58.jpg)
Example: Training and testing
• Key challenge: generalization to unseen examples
Training set (labels known) Test set (labels unknown)
![Page 59: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/59.jpg)
![Page 60: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/60.jpg)
Some classification methods
106 examples
Nearest neighbor
Shakhnarovich, Viola, Darrell 2003Berg, Berg, Malik 2005…
Neural networks
LeCun, Bottou, Bengio, Haffner 1998Rowley, Baluja, Kanade 1998…
Support Vector Machines and Kernels Conditional Random Fields
McCallum, Freitag, Pereira 2000Kumar, Hebert 2003…
Guyon, VapnikHeisele, Serre, Poggio, 2001…
![Page 61: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/61.jpg)
Classification … more soon
![Page 62: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/62.jpg)
Types of ML algorithms
• Unsupervised– Algorithms operate on unlabeled examples
• Supervised– Algorithms operate on labeled examples
• Semi/Partially-supervised– Algorithms combine both labeled and unlabeled
examples
![Page 63: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/63.jpg)
Supervised learning has many successes
• recognize speech,• steer a car,• classify documents• classify proteins• recognizing faces, objects in images• ...
Slide Credit: Avrim Blum
![Page 64: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/64.jpg)
However, for many problems, labeled data can be rare or expensive.
Unlabeled data is much cheaper.Need to pay someone to do it, requires special testing,…
Slide Credit: Avrim Blum
![Page 65: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/65.jpg)
However, for many problems, labeled data can be rare or expensive.
Unlabeled data is much cheaper.
Speech
Images
Medical outcomes
Customer modeling
Protein sequences
Web pages
Need to pay someone to do it, requires special testing,…
Slide Credit: Avrim Blum
![Page 66: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/66.jpg)
However, for many problems, labeled data can be rare or expensive.
Unlabeled data is much cheaper.
[From Jerry Zhu]
Need to pay someone to do it, requires special testing,…
Slide Credit: Avrim Blum
![Page 67: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/67.jpg)
Need to pay someone to do it, requires special testing,…
However, for many problems, labeled data can be rare or expensive.
Unlabeled data is much cheaper.
Can we make use of cheap unlabeled data?
Slide Credit: Avrim Blum
![Page 68: Machine Learning Overview Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart.](https://reader036.fdocuments.net/reader036/viewer/2022062305/5697bf7a1a28abf838c832f9/html5/thumbnails/68.jpg)
Semi-Supervised LearningCan we use unlabeled data to augment a
small labeled sample to improve learning?
But unlabeled data is missing the most important info!!
But maybe still has useful regularities that
we can use.
But…But…But…Slide Credit: Avrim Blum