1. Introduction to Machine Learning Algorithms

2. 2 What is Artificial Intelligence (AI)? Design and study of computer programs that behave intelligently. Designing computer programs to make computers smarter. Study of how to make computers do things at which, at the moment, people are better.

3. 3 Research Areas and Approaches Artificial Intelligence Research Rationalism (Logical) Empiricism (Statistical) Connectionism (Neural) Evolutionary (Genetic) Biological (Molecular) Paradigm Application Intelligent Agents Information Retrieval Electronic Commerce Data Mining Bioinformatics Natural Language Proc. Expert Systems Learning Algorithms Inference Mechanisms Knowledge Representation Intelligent System Architecture

4. 4 Concept of Machine Learning

5. 5

6. 6 Information Theory Context Computer Science (AI) Cognitive Science Statistics Machine Learning

7. 7 Why Machine Learning? Recent progress in algorithms and theory Growing flood of online data Computational power is available Budding industry Three niches for machine learning Data mining: using historical data to improve decisions  Medical records --> medical knowledge Software applications we can’t program by hand  Autonomous driving  Speech recognition Self-customizing programs  Newsreader that learns user interests

8. 8 Learning: Definition Definition  Learning is the improvement of performance in some environment through the acquisition of knowledge resulting from experience in that environment. the improvement of behavior on some performance task through acquisition of knowledge based on partial task experience

9. 9 A Learning Problem: EnjoySport Sky What is the general concept? TempHumidWindWaterForecastEnjoySports Sunny Warm Normal Strong Warm Same Yes Sunny Warm High Strong Warm Same Yes Rainy Cold High Strong Warm Change No Sunny Warm High Strong Cool Change Yes

10. 10 Metaphors and Methods Neurobiology Biological Evolution Heuristic Search Statistical Inference Memory and Retrieval Connectionist Learning Genetic Learning Tree / Rule Induction Case-Based Learning Probabilistic Induction

11. 11 What is the Learning Problem? Learning = improving with experience at some task  Improve over task T,  With respect to performance measure P,  Based on experience E. E.g., Learn to play checkers T: Play checkers P: % of games won in world tournament E: opportunity to play against self

12. 12 Machine Learning: Tasks Supervised Learning  Estimate an unknown mapping from known input- output pairs  Learn f w from training set D={(x,y)} s.t.  Classification: y is discrete  Regression: y is continuous Unsupervised Learning  Only input values are provided  Learn f w from D={(x)} s.t.  Compression  Clustering Reinforcement Learning

13. 13 Machine Learning: Strategies Rote learning Concept learning Learning from examples Learning by instruction Inductive learning Deductive learning Explanation-based learning (EBL) Learning by analogy Learning by observation

14. 14 Supervised Learning Given a sequence of input/output pairs of the form, where x i is a possible input and y i is the output associated with x i. Learn a function f that accounts for the examples seen so far, f(x i ) = y i for all i, and that makes a good guess for the outputs of the inputs that it has not seen.

15. 15 Examples of Input-Output Pairs Task Inputs Outputs Recognition Action Janitor robot problem Descriptions of objects Classes that the objects belong to Actions or predictions Descriptions of situations Descriptions of offices (floor, prof’s office) Yes or No (indicating whether or not the office contains a recycling bin)

16. 16 Unsupervised Learning Clustering  A clustering algorithm partitions the inputs into a fixed number of subsets or clusters so that inputs in the same cluster are close to one another. Discovery learning  The objective is to uncover new relations in the data.

17. 17 Online and Batch Learning Batch methods  Process large sets of examples all at once. Online (incremental) methods  Process examples one at a time.

18. 18 Machine Learning Algorithms and Applications

19. 19 Machine Learning Algorithms Neural Learning  Multilayer Perceptrons (MLPs)  Self-Organizing Maps (SOMs) Evolutionary Learning  Genetic Algorithms Probabilistic Learning  Bayesian Networks (BNs) Other Machine Learning Methods  Decision Trees (DTs)

20. 20 Neural Nets for Handwritten Digit Recognition … Pre-processing … … … … Input units Hidden units Output units 01239 … TrainingTest … … … 01239 ? …

21. 21 ALVINN System: Neural Network Learning to Steer an Autonomous Vehicle

22. 22 Learning to Navigate a Vehicle by Observing an Human Expert (1/2) Inputs  The images produces by a camera mounted on the vehicle Outputs  The actions taken by the human driver to steer the vehicle or adjust its speed. Result of learning  A function mapping images to control actions

23. 23 Learning to Navigate a Vehicle by Observing an Human Expert (2/2)

24. 24 Data Recorrection by a Hopfield Network original target data corrupted input data Recorrected data after 10 iterations Recorrected data after 20 iterations Fully recorrected data after 35 iterations

25. 25 ANN for Face Recognition 960 x 3 x 4 network is trained on gray-level images of faces to predict whether a person is looking to their left, right, ahead, or up.

26. 26 Data Mining -- -- -- Target data Cleaned data Transformed data Patterns/ model KnowledgeDatabase/data warehouse Selection & Sampling Selection & Sampling Preprocessing & Cleaning Preprocessing & Cleaning Transformation & reduction Transformation & reduction Interpretation/ Evaluation Interpretation/ Evaluation Data Mining Performance system

27. 27 Hot Water Flashing Nozzle with Evolutionary Algorithms Start Hot water enteringSteam and droplet at exit At throat: Mach 1 and onset of flashing Hans-Paul Schwefel performed the original experiments

28. 28 Machine Learning Applications in Bioinformatics

29. 29 Bayesian Networks for Gene Expression Analysis Processed data Data Preprocessing Learning algorithm Gene CGene B Gene A Target Gene D Gene CGene B Gene A Target Gene D Gene CGene B Gene A Target Gene D Gene CGene B Gene A Target Gene D The values of Gene C and Gene B are given. Belief propagation Probability for the target is computed. Learning Inference

30. 30 Multilayer Perceptrons for Gene Finding and Prediction Coding potential value GC Composition Length Donor Acceptor Intron vocabulary bases Discrete exon score 0 1 sequence score

31. 31 Self-Organizing Maps for DNA Microarray Data Analysis Two-dimensional array of postsynaptic neurons Bundle of synaptic connections Winning neurons Input

32. 32 Biological Information Extraction Text Data DB Location Date DB Record Database Template Filling Data Analysis & Field Identification Data Classification & Field Extraction Information Extraction Field Property Identification & Learning

33. 33 Biomolecular Computing 011001101010001ATGCTCGAAGCT