Nyhetsflöde
Logga in till din kurswebb
Du är inte inloggad på KTH så innehållet är inte anpassat efter dina val.
Har du frågor om kursen?
Om du är registrerad på en aktuell kursomgång, se kursrummet i Canvas. Du hittar rätt kursrum under "Kurser" i personliga menyn.
Är du inte registrerad, se Kurs-PM för DD2431 eller kontakta din studentexpedition, studievägledare, eller utbilningskansli.
I Nyhetsflödet hittar du uppdateringar på sidor, schema och inlägg från lärare (när de även behöver nå tidigare registrerade studenter).
Lärare Örjan Ekeberg redigerade 16 augusti 2013
TentamenExam
Schemahandläggare redigerade 31 augusti 2013
[u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-AS, TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-PRS, TCSCM1-SPR', u'TMAIM1-IR', u'TITMM2, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, TCSCM1-SPR, TITMM2, TKOMK3, TMAIM1, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Go to any of the rooms listed and ask. The persons in charge of the exams will have lists of all students registered for the exam and where they are supposed to sit.
Ok, thank you.
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 2, Concept Learning [Örjan]
Concept Learning Readings: Marsland, Chapter 1¶
* What is Concept Learning?
* Important terminology: positive and negative examples, hypotheses
* The structure of the hypotheses space: General and Special hypotheses
* How can one find a hypothesis that conforms with data?
* The Find-S algorithm
* Why does a naive List-Then-Eliminate algorithm not work in practice?
* How can the choise of leaning method influence the result?
* "Bias-Free Learning", is it possible? Is it desirable?
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Administratör Örjan Ekeberg redigerade 4 september 2013
Concept Learning Readings: Marsland, Chapter 1
* What is Concept Learning?
* Important terminology: positive and negative examples, hypotheses
* The structure of the hypotheses space: General and Special hypotheses
* How can one find a hypothesis that conforms with data?
* The Find-S algorithm
* Why does a naive List-Then-Eliminate algorithm not work in practice?
* How can the choise of leaning method influence the result?
* "Bias-Free Learning", is it possible? Is it desirable?
Slides on Concept Learning¶
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 10, Graphical Models [Atsuto]
Graphical Models Readings: Marsland, chapter 15¶
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* How can a hidden sequence be guessed?
* How can a Hidden Markov Model be constructed from data?
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Administratör Atsuto Maki redigerade 22 september 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* How can a hidden sequence be guessed?What is a Dynamic Bayesian network?
* How can a Hidden Markov Model be constructed from data
Lärare Atsuto Maki redigerade 9 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks¶
Lärare Atsuto Maki redigerade 9 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks
Lärare Atsuto Maki redigerade 9 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks¶
Lärare Atsuto Maki redigerade 9 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks
Slides on Dynamic Bayesian Networks
Lärare Atsuto Maki redigerade 10 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks¶ Slides on Dynamic Bayesian Networks
Lärare Atsuto Maki redigerade 10 oktober 2013
Graphical Models Readings: Marsland, chapter 15
* How can conditional probabilities be represented as a graph?
* What is a Bayesian network?
* What is a Hidden Markov Model?
* What is a Dynamic Bayesian network?
Slides on Bayesian Networks¶
Slides on Dynamic Bayesian Networks
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 8, Evolutionary Algorithms [Atsuto]
Genetic Algorithms Readings: Marsland, chapter 12¶
* In what way can evolution be regarded as an algorithm?
* What can be optimized using Genetic Algorithms?
* How are potential solutions represented?
* How do we represent the goal?
* Chromosomes, Populations and Generations
* What makes GA different from other optimization methods?
* What do we mean by Genetic Programming?
* What can go wrong?
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Atsuto Maki redigerade 2 oktober 2013
Genetic Algorithms Readings: Marsland, chapter 12
* In what way can evolution be regarded as an algorithm?
* What can be optimized using Genetic Algorithms?
* How are potential solutions represented?
* How do we represent the goal?
* Chromosomes, Populations and Generations
* What makes GA different from other optimization methods?
* What do we mean by Genetic Programming?
* What can go wrong?
Slides on Genetic Algorithm¶
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 6, Ensamble Learning [Atsuto]
Bagging and Boosting Readings: Marsland, Chapter 7¶
* Bagging
* Boosting
* Bayes MAP Hypothesis
* Boosted Bayes MAP Hypothesis
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Administratör Atsuto Maki redigerade 22 september 2013
Bagging and Boosting Readings: Marsland, Chapter 7
* BaggingWisdom of Crowd
* Boosting
* Bayes MAP Hypothesis
* Boosted Bayes MAP Hypothesis
* Characterization of classifiers
* Bagging
* Boosting
Lärare Atsuto Maki redigerade 26 september 2013
Lecture 6, Ensaemble Learning [Atsuto]
Bagging and Boosting Readings: Marsland, Chapter 7
* Wisdom of Crowd
* Characterization of classifiers
* Bagging
* Boosting
Slides on Ensemble Learning
Lärare Atsuto Maki redigerade 27 september 2013
Bagging and Boosting Readings: Marsland, Chapter 7
* Wisdom of Crowd
* Characterization of classifiers
* Bagging
* Boosting
Slides on Ensemble Learning¶
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 12, Summary and Outlook
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 18 oktober 2013
Slides, summary of Örjans lectures¶
Lärare Atsuto Maki redigerade 19 oktober 2013
Slides, summary of Örjans lectures
Slides, summary of Atsuto's lectures (extractions) ¶
¶
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 11, Learning Theory [Örjan]
Learning Theory
* Is it possible to measure how hard a learning task is?
* What can go wrong during learning?
* What do we mean when we say that a hypothesis is "approximately correct"
* Is it possible to estimate the number of traning examples needed?
* Are there learning tasks that take exponentially long time?
* PAC-learnable
* VC-dimension
* Can we estimate how many errors a learner must make?
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 12 oktober 2013
Learning Theory
* Is it possible to measure how hard a learning task is?
* What can go wrong during learning?
* What do we mean when we say that a hypothesis is "approximately correct"
* Is it possible to estimate the number of traning examples needed?
* Are there learning tasks that take exponentially long time?
* PAC-learnable
* VC-dimension
* Can we estimate how many errors a learner must make?
Slides on Learning Theory¶
Schemahandläggare ställde in händelsen 14 december 2013
Schemahandläggare redigerade 15 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 16 augusti 2013
FöreläsningLecture 9, Reinforcement Learning [Örjan]
Reinforcement Learning Readings: Marsland: chapter 13¶
* Is it possible to learn when nobody tells you the correct answer?
* Central terms: State, Action, Reward
* More terms: Value function (cumulative value), Policy
* How can we judge the consequences of our actions?
* What do we mean by an optimal behavior?
* Is it possible to learn what is the best thing to do in each state?
* Is it possible to learn faster by planning ahead?
Schemahandläggare redigerade 31 augusti 2013
[u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', u'TMAIM1-IR', u'TCSCM1-AS, TCSCM1-BER, TCSCM1-PRS, TCSCM1-SPR, TITMM2, TIVNM1, TKOMK3, TMAIM1, TMAIM1-BIO', u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2
Schemahandläggare redigerade 14 september 2013
TCSCM1-AS, [u'TIVNM1', u'TKOMK3', u'TMAIM1-PC', u'TCSCM1-BER', TCSCM1-PRS, u'TMAIM1', u'TCSCM1-AS', u'TCSCM1-SPR', TITMM2, TIVNM1, TKOMK3, TMAIM1, u'TMAIM1-IR', u'TMAIM1-BIO', TMAIM1-IR, TMAIM1-PC, TSCRM1, TSCRM2u'TSCRM1', u'TITMM2', u'TSCRM2', u'TCSCM1-PRS']
Lärare Örjan Ekeberg redigerade 4 oktober 2013
Reinforcement Learning Readings: Marsland: chapter 13
* Is it possible to learn when nobody tells you the correct answer?
* Central terms: State, Action, Reward
* More terms: Value function (cumulative value), Policy
* How can we judge the consequences of our actions?
* What do we mean by an optimal behavior?
* Is it possible to learn what is the best thing to do in each state?
* Is it possible to learn faster by planning ahead?
Slides on Reinforcement Learning¶
Hey,
How can we know in wich room to go for the exam ?