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Vecka 36 2015			Visa i Mitt schema

	Tis 1 sep 10:00-12:00	Lecture 1, Introduction
	HT 2015 Föreläsning Lärare: Giampiero Salvi, Atsuto Maki, Örjan Ekeberg Plats: F1	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 3 sep 17:00-19:00	Lecture 2, Decision Trees
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: E1	Anmärkning: Flyttad från kl 13-15 pga för liten sal Topics: 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 2015			Visa i Mitt schema

	Tis 8 sep 10:00-12:00	Lecture 3, Challenges to machine learning
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: D2	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 3 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 10 sep 13:00-15:00	Lecture 4, Regression Introduction
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: M2	Topics: Function approximation Linear regression RANSAC Nearest Neighbours regression Linear regression + regularization Ridge regression Lasso Slides for Lecture 4 Related reading: Chapter 3.1, 3.2, 3.5 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 38 2015			Visa i Mitt schema

	Tis 15 sep 10:00-12:00	Lecture 5, Probability I
	HT 2015 Föreläsning Lärare: Giampiero Salvi Plats: FR4	Topics: concepts of probability theory random variables and distributions conditional probabilities and Bayes rule Bayesian inference Slides for Lecture 5 Related reading: Prince, S.J.D., Part I (Chapters 2, 3, 5) If you want to know more a great book on these topics is Bishop, C. M. Pattern Recognition and Machine Learning, Springer.
	Tors 17 sep 13:00-15:00	Lecture 6, Probability II
	HT 2015 Föreläsning Lärare: Giampiero Salvi Plats: F2	Topics: estimation theory Maximum Likelihood, Maximum a Posteriori, Bayesian estimation Unsupervised learning and K-means Mixture of distributions and Expectation Maximization algorithm Slides for Lecture 06 Recommended reading: Prince, Chapter 3, 4, 7.4. (Book available in full PDF here)
Vecka 39 2015			Visa i Mitt schema

	Tis 22 sep 10:00-12:00	Lecture 7, Classification Introduction
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: K1	Topics: Naive Bayes Logistic regression Inference and decision Discriminative function Discriminative vs Generative model Slides for Lecture 7 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 24 sep 13:00-15:00	Lecture 8, Classification with Separating Hyperplanes
	HT 2015 Föreläsning Lärare: Örjan Ekeberg Plats: FR4	Topics: Linear separation in high dimensional spaces Structural risk minimization Support Vector Machines Kernels for separating in a higher dimensional space Non-separable classes Slides from lecture 8 2 kommentarer
Vecka 40 2015			Visa i Mitt schema

	Tis 29 sep 10:00-12:00	Lecture 9, Artificial Neural Networks
	HT 2015 Föreläsning Lärare: Örjan Ekeberg Plats: FR4	Topics: Feed forward networks Using multiple processing layers Learning with Backprop Deep networks Slides from lecture 9
	Tors 1 okt 13:00-15:00	Lecture 10, Ensemble Methods
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: M1	Topics: Why combine classifiers? Bagging Decision Forests Boosting Slides for Lecture 10 Slides for Lecture 10 (full size) 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 2015			Visa i Mitt schema

	Tis 6 okt 10:00-12:00	Lecture 11, Dimensionality Reduction
	HT 2015 Föreläsning Lärare: Atsuto Maki Plats: M2	We are going to revisit/resume Boosting at the beginning of the lecture. Topics: Unsupervised Learning Principal Component Analysis (PCA) Concept of subspace Similarity measures Subspace methods 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/
	Tors 8 okt 13:00-15:00	Lecture 12, Summary
	HT 2015 Föreläsning Lärare: Giampiero Salvi, Atsuto Maki, Örjan Ekeberg Plats: FR4	Slides for Lecture 12 Note: The scope of the exam is what has been covered in Lecture 1-11. 3 kommentarer

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