Machine Learning ICS 178

1 / 15

# Machine Learning ICS 178 - PowerPoint PPT Presentation

Machine Learning ICS 178. Instructor: Max Welling . What is Expected?. Class Homework/Projects (40%) Midterm (20%) Final (40%). For the projects, students should make teams. This class needs your active participation: please ask questions and

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## Machine Learning ICS 178

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
1. Machine LearningICS 178 Instructor: Max Welling

2. What is Expected? • Class • Homework/Projects (40%) • Midterm (20%) • Final (40%) • For the projects, students should make teams. • This class needs your active participation: please ask questions and • participate in discussions (there is no such thing as a dumb question).

3. Syllabus • 1: Introduction: overview, examples, goals, probability, conditional independence, matrices, eigenvalue decompositions • 2: Optimization and Data Visualization: Stochastic gradient descent, coordinate descent, centering, sphering, histograms, scatter-plots. • 3: Classification I: emprirical Risk Minimization, k-nearest neighbors, decision stumps, decision tree. • 4: Classification II: random forests, boosting. • 5: Neural networks: perceptron, logistic regression, multi-layer networks, back-propagation. • 6: Regression: Least squares regression. • 7: Clustering: k-means, single linkage, agglomorative clustering, MDL penalty. • 8: Dimesionality reduction: principal components analysis, Fisher linear discriminant analysis. • 9: Reinforcement learning: MDPs, TD- and Q-learning, value iteration. • 10: Bayesian methods: Bayes rule, generative models, naive Bayes classifier.

4. Machine Learningaccording to • The ability of a machine to improve its performance based on previous results. • The process by which computer systems can be directed to improve their • performance over time. Examples are neural networks and genetic algorithms. • Subspecialty of artificial intelligence concerned with developing methods for software • to learn from experience or extract knowledge from examples in a database. • The ability of a program to learn from experience — • that is, to modify its execution on the basis of newly acquired information. • Machine learning is an area of artificial intelligence concerned with the • development of techniques which allow computers to "learn". • More specifically, machine learning is a method for creating computer • programs by the analysis of data sets. Machine learning overlaps heavily • with statistics, since both fields study the analysis of data, but unlike statistics, • machine learning is concerned with the algorithmic complexity of computational implementations. ...

5. Some Examples • ZIP code recognition • Loan application classification • Signature recognition • Voice recognition over phone • Credit card fraud detection • Spam filter • Suggesting other products at Amazone.com • Marketing • Stock market prediction • Expert level chess and checkers systems • biometric identification (fingerprints, DNA, iris scan, face) • machine translation • web-search • document & information retrieval • camera surveillance • robosoccer • and so on and so on...

6. Can Computers play Humans at Chess? • Chess Playing is a classic AI problem • well-defined problem • very complex: difficult for humans to play well • Conclusion: YES: today’s computers can beat even the best human Garry Kasparov (current World Champion) Deep Blue Deep Thought Points Ratings

7. 2005 DARPA Grand Challenge The Grand Challenge is an off-road robot competition devised by DARPA (Defense Advanced Research Projects Agency) to promote research in the area of autonomous vehicles. The challenge consists of building a robot capable of navigating 175 miles throughdesert terrain in less than 10 hours, with no human intervention. http://www.grandchallenge.org/

8. 2007 Darpa Challenge http://www.darpa.mil/grandchallenge/overview.asp

9. Netflix Challenge http://www.netflixprize.com/leaderboard • Netflix awards \$1M for the person who improves their system by 10%. • The relevant machine learning problem goes under then name: • “user recommendation system” or “collaborative filtering”. • When you shop online at Amazon.com they recommend books based on • what links you are clicking. • For netflix the relevant problem is predicting movie-rating values for users. total of +/- 400,000,000 nonzero entries (99% sparse) movies (+/- 17,770) users (+/- 240,000)

10. source: http://www.netflixprize.com/community/viewtopic.php?id=103 Netflix Challenge # movies # movies with that mean # ratings mean movie rating value # users with that mean # users # ratings mean user rating value

11. The Task • The user-movie matrix has many missing entries: Joe did not happen to rate “ET”. • Netflix wants to recommend unseen movies to users based on movies he/she • has seen (and rated!) in the past. • To recommend movies we are being asked to fill in the missing entries for Joe • with predicted ratings and pick the movies with the highest predicted ratings. • Where does the information come from? • Say we want to predict the rating for Joe and ET. • I: Mary has rated all movies that Joe has seen in the past very similarly. • She has also seen ET and rated it with a 5. What would you predict for Joe? • II: StarTrek that has obtained very similar ratings as ET from all users. • StarTrek was rated 4 by Joe. What would you predict for ET?

12. Your Homework & Project • You will team up with 1 or more partners and implement algorithms that we • discuss in class on the netflix problem. • Our goal is to get high up on the leaderboard • This involves both trying out various learning techniques (machine learning) • as well as dealing with the large size of the data (data mining). • Towards the end we will combine all our algorithms to get a final score. • Every class (starting next week) we will have a presentation by 1 team to report • on their progress and to share experience. • Read this article on how good these systems can be:http://www.theonion.com/content/node/57311?utm_source=onion_rss_daily

13. Text Data • Text corpora are widely available in digital form these days (scanned journals, • scanned newspapers, blogs,...). • We can mine this text and discover interesting patterns: what topics are present • in this article, what is the most similar/relevant article/webpage in the corpus. • Here the data has a very similar format: 99% sparse word-tokens (+/- 20,000) documents (up to 1000,000)

14. Text Data • Each document is represented as a count vector for each of the words in the • vocabulary: [20,5,3,0,1,0,2,0,0,0,5,0,...]. • So, in the article the word “president” appeared 5 times (can you guess a topic?). • Now, we don’t want to fill in missing entries (sparse means “0”, not missing). • Our task is to find for instance which documents are most similar • (document retrieval). • Many more data matrices have the same format: for instance gene-expression • data is a matrix of genes vs. experiments where the values represent the • “activity level” of the gene in that experiment. Can we identify diseases? “president” “the”

15. Why is this cool/important? • Modern technologies generate data at an unprecedented scale. • The amount of data doubles every year. • “One petabyte is equivalent to the text in one billion books, • yet many scientific instruments, including the Large Synoptic Survey Telescope, • will soon be generating several petabytes annually”. • (2020 Computing: Science in an exponential world:Nature Published online: 22 March 2006) • Computers dominate our daily lives • Science, industry, army, our social interactions etc. • We can no longer “eyeball” the images captured by some satellite • for interesting events, or check every webpage for some topic. • We need to trust computers to do the work for us.