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Section for Cognitive Systems
DTU Compute

02450 Introduction to Machine Learning and Data Mining

Tue Herlau
Tue Herlau
 
Morten Mørup
Morten Mørup
 
Jes Frellsen
Jes Frellsen
 
Mikkel N. Schmidt
Mikkel N. Schmidt
 
Rune Dodensig Kjærsgaard
Rune Dodensig Kjærsgaard
 
Nikolaos Nakis
Nikolaos Nakis
 
Alison Marie Sandrine Pouplin
Alison Marie Sandrine Pouplin
 
Oliver Kinch Hermansen
Oliver Kinch Hermansen
 
Jonas Søbro Christophersen
Jonas Søbro Christophersen
 
Oldouz Majidi
Oldouz Majidi
 
Peter Mørch Groth
Peter Mørch Groth
 
Rasmus Hannibal Tirsgaard
Rasmus Hannibal Tirsgaard
 
Michael Rahbek
Michael Rahbek
 
Qahir Siavash Yousefi
Qahir Siavash Yousefi
 
Fabian Martin Mager
Fabian Martin Mager
 
David Ribberholt Ipsen
David Ribberholt Ipsen
 
Jorge Sintes Fernandez
Jorge Sintes Fernandez
 
Alvaro Carrera Cardeli
Alvaro Carrera Cardeli
 

Machine learning and data mining

The course is designed around a data modeling framework shown in the figure. Each lecture/assignment will focus on an aspect of the data modeling framework.

data modeling framework

We emphasize the holistic view of modeling in order to motivate and stress the relevance of individual components and building blocks, disseminate the obtained competence (see the course learning obejctives), and make them applicable for a broad spectrum of engineering problems in e.g. biomedical engineering, chemistry, electrical engineering, and informatics.

Resources

DTU Learn

If you are enrolled in the course you can access material and participate in the course through the DTU Learn homepage.

Lectures

The lectures will take place Tuesdays from 13:00-15:00. Due to coronavirus the lectures will be online in the first few weeks of the semester. Information about streaming and access will be available on DTU Learn.

Exercises

Exercises will take place after lectures Tuesdays from 15:00-17:00. Format will be subject to change as dictated by the coronavirus situation. As of right now, exercises will be held on Microsoft teams.

  • Exercise team Turing, (Python):
  • Exercise team Neumann, (Python):
  • Exercise team Minsky, (R+Python):
  • Exercise team Bayes, (Matlab+Python):
  • Exercise team Rosenblatt, (Python):
  • Reading material, lecture slides and exercises

    The course will use lecture notes and other freely available material. Lecture notes, slides, course assignment instructions etc. is available at the DTU learn course page (requires formal enrolment to the course).

    Online demos

    We have developed several online demos which illustrates key concepts from the course. The topics discussed currently includes PCA, regression, classification and density estimation.

    Course description

    A description of the course can be found at the DTU Coursebase

    Online help and support

    Online help and support is available through the DTU Learn discussion forum.

    Teachers

    Lecture schedule

    No. Date Subject Reading Homework
    12 February, 2021 THIntroduction C1
    Data: Feature extraction, and visualization
    29 February, 2021 THData, feature extraction and PCA C2, C3 P3.1, P2.1, P3.2
    316 February, 2021 THMeasures of similarity, summary statistics and probabilities C4, C5 P4.1, P4.2, P4.3
    423 February, 2021 THProbability densities and data visualization C6, C7 P6.1, P6.2, P7.1
    Supervised learning: Classification and regression
    52 March, 2021 THDecision trees and linear regression C8, C9 P9.1, P8.1, P8.2
    69 March, 2021 THOverfitting, cross-validation and Nearest Neighbor (Project 1 due before 13:00) C10, C12 P10.1, P10.2, P12.1
    716 March, 2021 THPerformance evaluation, Bayes, and Naive Bayes C11, C13 P13.1, 13.2, P12.2
    823 March, 2021 THArtificial Neural Networks and Bias/Variance C14, C15 P15.1, P15.2, P15.3
    Holiday
    96 April, 2021 THAUC and ensemble methods C16, C17 P16.1, P16.2, P17.1
    Unsupervised learning: Clustering and density estimation
    1013 April, 2021 THK-means and hierarchical clustering C18 P18.1, P18.2, P18.3
    1120 April, 2021 THMixture models and density estimation (Project 2 due before 13:00) C19, C20 P20.1, P19.1, P19.2
    1227 April, 2021 THAssociation mining C21 P21.1, P18.2, P18.3
    Recap
    134 May, 2021 THRecap and discussion of the exam C1-C21

    (Cx refers to Chapter x of the course notes. Px.y refers to problem number y in chapter x of the course notes.
    The first listed problem will be that weeks discussion question at the exercises.)

    FAQ

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