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Gruppwebben som samarbetsyta stängs 31 december 2026. Du som administratör behöver nu exportera gruppens innehåll och/eller radera gruppen.

Observera: Från och med den 1 oktober 2026 kommer delar av gruppwebben som inte längre används, successivt att stängas ned, exempelvis gruppwebbar som redan har flyttats eller varit inaktiva under en längre tid.

Mer information hittar du i nyheten: Gruppwebben och Social stänger hösten 2026. Stöd och instruktioner för hur du exporterar en gruppwebb finns i: Gruppwebben som samarbetsyta stängs hösten 2026.

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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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