1. Machine Learning Group University College Dublin 4.30 Machine Learning Pádraig Cunningham

2. Intro to ML 2 Outline Week 1  Introduction & General Overview of Matrix Decomposition  Nearest Neighbour Classifiers  Tutorial Week 2: Neural Networks  Simple Perceptron, Backpropagation  Other Architectures: Hopfield, Self-Organising Maps  Tutorial Week 3  Support Vector Machines  Kernel Methods & Evaluation  Tutorial Week 4  Decision Trees  Naïve Bayes  Tutorial

3. Intro to ML 3 Outline Week 5: Ensemble Techniques  Bagging  Boosting  Tutorial Week 6: Unsupervised Learning  Hierarchical Clustering  Other Clustering Algorithms: k-Means, Spectral Clustering  Tutorial Week 7: Dimension Reduction  Principle Components Analysis, LSI, SVD  Feature Selection  Tutorial Later  2 revision tutorials Coursework 3-4 pieces, 15 hours, Weka & Java

4. Intro to ML 4 Why Machine Learning Recent progress in algorithms and theory Loads of processing power Computational power is available Growing flood of online data  Amazon  Google

5. Intro to ML 5 3 niches for ML Data mining: using historical data to improve decisions  medical records  medical knowledge Software applications that cannot be programmed by hand.  autonomous driving  speech recognition  i.e. weak theory domains. Self customising programs  Personalised Newspaper  E-mail filtering

6. Intro to ML 6 Data-mining in medical records Quality Assurance in Maternity Care. http://svr-www.eng.cam.ac.uk/projects/qamc/qamc.html

7. Intro to ML 7 Rule Learning The QAMC system uses Decision /trees (I think!) It is also possible to extract rules from data:- IfNo previous normal delivery, and Abnormal 2 nd Trimester Ultrasound, and Malpresentation at admission Then Probability of Emergency C-Section is 0.6 Over training dat 26/41 = 0.63 Over test data: 12/20 = 0.6

8. Intro to ML 8 Spam Filtering For Machine Learning…  Lots of training data  High dimensionality data (lots of features)  Email is a diverse concept Porn, mortgage, religion, cheap drugs… Work, family, play… Spam Filtering is a challenge because…  Arms race: spammers vs filters  False Positives are unacceptable Spam is a changing concept

9. Intro to ML 9 ALVIN Problems too difficult to program by hand Alvin drives at 70mph on motorways

10. Intro to ML 10 Autonomous Vehicles DARPA Grand Challenge 2005  Winner: Stanley from Stanford Various modules use ML

11. Intro to ML 11 SmartRadio Internet-based music radio Personalised  Collaborative Recommendation  Content-Based Recommendation supported by knowledge discovery from log data supported by feature extraction from sound files  feature seleciton  refinement

12. Intro to ML 12 Smart Radio Smart Radio is a web based client-server music application which allows listeners build, manage and share music programmes The project was set up to look at a possible model for:  The regulated distribution of music on the web  A personalised stream of music service  To provide an architecture and data to test our data mining and collaborative filtering algorithms

13. Intro to ML 13 ML Dimensions Lazy v’s Eager  k-NN v’s rule learning Supervised v’s Unsupervised Symbolic v’s Sub-symbolic