Outline

Credit Points

Machine Learning (CS331)

T.Vetter,

T.Albrecht,R.Knothe,M.Luthi

March 1,2010

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Machine Learning (CS331)

Docent Prof.Dr.Thomas Vetter

Assistants Dr.Reinhard Knothe,Thomas Albrecht,Marcel Luthi

Schedule Lectures:Monday 14-16

Thursday 10-12

Exercise:Monday 16-18

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

The course is divided into two parts.

Part I

theoretical foundations of machine learning.

precise,mathematical setting of the learning problem

Part II

focus on a practical application of machine learning.

Automatic face recognition

prior-knowledge about human faces:Models are learned from

a dataset of example faces.

Registration

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

Part I:Theory

Lectures:Mo/Do

Excercise:Mo

Correction/Discussion

homework

Script/Slides

Part II:practical application

the student have to read

5 scientic papers.

Lectures/Discussion

about the papers:

Mo/Do

Excercise:Mo

Correction/Discussion

homework

Slides/Papers

We expect that the students rework the lecture,read the papers

and do the homework.

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

How to get the Credit Points?

50% of the homework

oral exam

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

How to get the Credit Points?

50% of the homework

oral exam

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

Outline

Credit Points

How to get the Credit Points?

50% of the homework

oral exam

T.Vetter,T.Albrecht,R.Knothe,M.Luthi

Machine Learning (CS331)

## Comments 0

Log in to post a comment