Makine Öğrenmesine Giriş. YZM 3226 Makine Öğrenmesi

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1 Makine Öğrenmesine Giriş YZM 3226 Makine Öğrenmesi

2 Sunum İçeriği Makine Öğrenmesi Nedir? Neden Önemlidir? Makine Öğrenmesi Tarihi Makine Öğrenmesi Örnek Uygulamaları Öğrenme Tipleri

3 Öğrenme Nedir? Reasoning Düşünme) Inference (Akıl Yürütme, Mantıklı (Anlam Çıkartma) Generalization (Genelleme) Experimenting (Geçmiş Deneyimlerden Yararlanma) Induction (Tümevarım)

4 Bir Öğrenme Örneği 1. yumurtayı yere attığımda kırıldı. 2. yumurtayı yere attığımda kırıldı yumurtayı yere attığımda kırıldı. Buna göre herhangi bir yumurtayı yere atarsam kırılır.

5 Bir Öğrenme Örneği Örnek soru: Güneş yarın sabah doğacak mı? Neden?

6 Bir Öğrenme Örneği Örnek soru: Güneş yarın sabah doğacak mı? Neden? Cevap: Çünkü milyonlarca yıldır her sabah aynı şekilde doğdu. Yarın sabah da doğmaması için bir sebep yok.

7 Bir Öğrenme Örneği Örnek soru: Güneş yarın sabah doğacak mı? Neden? Cevap: Çünkü milyonlarca yıldır her sabah aynı şekilde doğdu. Yarın sabah da doğmaması için bir sebep yok. Durum analizi: Dünyaya göktaşı çarpıp yörüngesini değiştirebilir. Güneş patlayabilir. Uzaylılar gelip güneşi karartabilirler., vb.

8 İnsan Öğrenmesi Alışkanlık yolu ile öğrenme Görerek öğrenme Talimatlardan öğrenme Örneklerden öğrenme Benzetim (Analoji) yolu ile öğrenme Açıklamalardan öğrenme Deney yolu ile öğrenme Keşfetmek yolu ile öğrenme

9 Makine Öğrenmesi Nedir?

10 What is Machine Learning? Machine Learning is programming computers to optimize a performance criterion using example data or past experience.

11 What is Machine Learning?

12 Machine Learning and Data Mining

13 Machine Learning and Data Mining The two areas overlap in many ways Data Mining uses many machine learning methods. Machine Learning also employs data mining methods as "unsupervised learning" and as a preprocessing step to improve learner accuracy.

14 Why Machine Learning? Learning is used when: Human expertise does not exist (e.g. navigating on Mars) Humans are unable to explain their expertise (e.g.sales in supermarket) Solution changes in time (e.g. routing on a computer network) Solution needs to be adapted to particular cases (e.g. user Biometrics)

15 Why Machine Learning? What we talk about when we talk about Learning Learning general models from a data of particular examples Build a model that is a good and useful approximation to the data. Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce. Example: Customer transactions to consumer behavior:

16 History of Machine Learning 1950s: Samuel s checker player Selfridge s Pandemonium 1960s: Evolutionary learning Neural networks: Perceptron Pattern recognition Learning in the limit theory Minsky and Papert prove limitations of Perceptron 1970s: Symbolic concept induction Winston s arch learner Expert systems and the knowledge acquisition bottleneck Quinlan s ID3 Michalski s AQ and soybean diagnosis Scientific discovery with BACON Mathematical discovery with AM

17 History of Machine Learning 1980s: Advanced decision tree and rule learning Explanation-based Learning (EBL) Learning and planning and problem solving Utility problem Analogy Cognitive architectures Resurgence of neural networks (connectionism, backpropagation) Valiant s PAC Learning Theory Focus on experimental methodology 1990s: Data mining Adaptive software agents and web applications Text learning Reinforcement learning (RL) Inductive Logic Programming (ILP) Ensembles: Bagging, Boosting, and Stacking Bayes Net learning

18 History of Machine Learning 2000s: Support vector machines Kernel methods Graphical models Statistical relational learning Transfer learning Sequence labeling Collective classification and structured outputs Computer Systems Applications Compilers Debugging Graphics Security (intrusion, virus, and worm detection) E mail management Personalized assistants that learn Learning in robotics and vision

19 Sample of ML Applications Speech and hand-writing recognition Autonomous robot control Data mining and bioinformatics Playing games Fault detection Clinical diagnosis Spam detection Credit scoring, fraud detection Web mining: search engines Counter-terrorism Online tutoring systems Market basket analysis Applications are diverse but methods are generic

20 Example of Patterns Examples of patterns: sound wave, fingerprint, trees, face, bar code, and character images.

21 Sample of ML Applications

22 Sample of ML Applications Çin de pilot uygulama Bir şehrin mahkeme hakimleri bir bilgisayar programı Amaç: Daha adil bir dünya ALVIN: Otobanda saatte 70 mil hızla sürücüsüz gidebilen bir otomobil Bütün denemeler trafiğe kapalı alanlarda gerçekleştirilmiştir

23 Autonomous Land Vehicle (DARPA s GrandChallenge contest)

24 Sample of ML Applications

25 Sample of ML Applications

26 Sample of ML Applications

27 Sample of ML Applications

28 Sample of ML Applications

29 Recommender Systems

30 Machine Translation

31 The Chinese Room

32 KLAIR Project Mark Huckvale (University College London), Ian Howard (Cambridge), Sascha Fagel (Berlin)

33 Sample of ML Applications

34 Sample of ML Applications

35 Machine Leaning is easy? 1960 s, a famous AI professor from MIT said: At the end of the summer, we will have developed an electronic eye 2002 road traffic monitoring, facial recognition, some medical image analysis, part inspection, etc In 1958, Herbert Simon (CMU) predicted that within 10 years a computer would be Chess champion This prediction became true in 1998 Today, computers have won over world champions in several games, including Checkers, Othello, and Chess, but still do not do well in Go In the 70 s, many believed that computer-controlled robots would soon be everywhere from manufacturing plants to home Today, some industries (automobile, electronics) are highly robotized, but home robots are still a thing of the future.

36 Learning Rules Online Learning: policy learning and execution at the same time Offline Learning: learn policy first given training data, and then execute policy without modifying the policy

37 Learning Types Supervised(Gözetimli) Learning Learning mapping between input x and desired output y Teacher gives me y s for the learning purposes Unsupervised(Gözetimsiz) Learning No specific outputs given by a teacher Learning relations between data components Reinforcement Learning (Destekli) Learning mapping between input x and desired output y Critic does not give me y s but instead a signal of how good my answer was (for example true or false) Other Types Learning associations, concept learning, explanation-based learning, etc.

38 Learning Learning Supervised Learning Classification Unsupervised Learning Clustering Decision Tree Learning Bayesian Learning Nearest Neighbour Neural Networks Support Vector Machines Regression Association Analysis Sequence Analysis Summerization Descriptive Statistics Outlier Analysis Scoring

39 ML Techniques Fuzzy Logic Bayes Theorem Genetic Algorithms Linear and nonlinear regression Adaptive Control Theory Neural Networks Self Organizing Maps Statistical Learning Theory Rule Induction Decision Tree Generation

40 Text Books First Edition 2004 Second Edition 2010 MIT Press

41 Final Comments Machine learning algorithms play an important role in industrial applications, commercial data analysis and especially data mining applications.

42 Data Analysis Tool and Dataset Repository Examples

43 Örnek Projeler Machine Learning in Cloud Computing Kinect and Machine Learning Machine Learning for Medical Diagnosis Pattern Recognition in Music An application of Machine Learning to a Mobile Game Machine Learning Based Recommendation System Network Intrusion Detection Using Machine Learning Techniques Machine Learning for Social Network Analysis (Facebook, Twitter, Linkedin etc.) Machine Learning for Robotics Recognition Systems (Hand recognition, Speech recognition etc.) Machine Learning Applied to Human Brain

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