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Vecka 36 2014			Visa i Mitt schema

	Mån 1 sep 17:00-19:00	Lecture 1, Introduction
	HT 2014 Föreläsning Plats: E1	Structure of the Course What will the course cover? How are labs and examination handled? Learning Machines What do we mean by a "Learning Machine"? Supervised vs Unsupervised learning? What can learning algorithms be used for? How can a simple learning program be constructed? What is a Nearest neighbour classifier? Slides for Lecture 1 Slides for Lecture 1 (part II)
	Tors 4 sep 10:00-12:00	Lecture 2, Decision Trees
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: V2	What is a Decision Tree? When are decision trees useful? How can one select what questions to ask? What do we mean by Entropy for a data set? What do we mean by the Information Gain of a question? What is the problem of overfitting? Minimizing training error? What extensions will be possible for improvement? Slides for Lecture 2 Related reading: Chapter 8.1 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
Vecka 37 2014			Visa i Mitt schema

	Mån 8 sep 15:00-17:00	Lecture 3, Probability I
	HT 2014 Föreläsning Lärare: Giampiero Salvi Plats: FR4	Topics: Probability theory Common probability distributions Bayes rule and machine learning Fitting probability models Handouts: 03-probtheory-2x2.pdf Selected reading: Chapters 2-5 from Computer Vision: Models, Learning, and Inference Simon J.D. Prince available online at: http://www.computervisionmodels.com/
	Tors 11 sep 10:00-12:00	Lecture 4, Probability II
	HT 2014 Föreläsning Lärare: Giampiero Salvi Plats: V2	Teacher: Giampiero Salvi Topics: Fitting probability models (continued) Model selection (Occam's Razor) Unsupervised learning and Expectation Maximization Selected reading: Chapters 4 and 7 from Computer Vision: Models, Learning, and Inference Simon J.D. Prince available online at: http://www.computervisionmodels.com/ Handouts: Handouts for Lecture 04
Vecka 38 2014			Visa i Mitt schema

	Mån 15 sep 17:00-19:00	Lecture 5, Challenges to machine learning
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: E1	Topics: Challenges to machine learning Model complexity and overfitting The curse of dimensionality Concepts of prediction errors The bias-variance trade-off Slides for Lecture 5 Related reading: Chapter 2 and 6.4 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
	Tors 18 sep 10:00-12:00	Lecture 6, Regression Introduction
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: FR4	Topics: Function approximation Linear regression RANSAC Nearest Neighbours regression Linear regression + regularization Ridge regression Lasso Slides for Lecture 6 Related reading: Chapter 3 and 6.2 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
Vecka 39 2014			Visa i Mitt schema

	Mån 22 sep 17:00-19:00	Lecture 7, Classification Introduction
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: K1	Topics: Naive Bayes Logistic regression Inference and decision Discriminative function Discriminative vs Generative model Slides for Lecture 7 (part I) , Slides for Lecture 7 (part II) Related reading: Chapter 4 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
	Tors 25 sep 08:00-10:00	Lecture 8, Classification with Separating Hyperplanes
	HT 2014 Föreläsning Lärare: Örjan Ekeberg Plats: FR4	Topics: Linear separation Structural risk minimization Support vector machines Kernels Non-separable Classes Slides from lecture 8
Vecka 40 2014			Visa i Mitt schema

	Mån 29 sep 17:00-19:00	Lecture 9, Learning Theory
	HT 2014 Föreläsning Lärare: Örjan Ekeberg Plats: E1	Topics: Concepts and Hypotheses PAC-Learning VC-Dimension Slides from lecture 9
	Tors 2 okt 10:00-12:00	Lecture 10, Ensemble Methods
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: FR4	Topics: Wisdom of Crowd Why combine classifiers? Bagging Decision Forests Boosting Slides for Lecture 10 Related reading: Chapter 8.2 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
Vecka 41 2014			Visa i Mitt schema

	Mån 6 okt 15:00-17:00	Lecture 11, Dimensionality Reduction
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: Q1	Topics: Concept of subspace Similarity measure Subspace method Unsupervised Learning Principal Component Analysis (PCA) Slides for Lecture 11 Related reading: Chapter 10.2 from An Introduction to Statistical Learning (Springer, 2013) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Available online: http://www-bcf.usc.edu/~gareth/ISL/
Vecka 42 2014			Visa i Mitt schema

	Mån 13 okt 17:00-19:00	Lecture 12, Summary
	HT 2014 Föreläsning Lärare: Atsuto Maki Plats: D1	Slides for Lecture 12

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