SF2957 HT23 Statistical Machine Learning (50667)

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SF2957 HT21 Statistical Machine Learning

Lecturer: Adam Lindhe. Email: adlindhe@kth.se

Teaching assistant: Viktor Nilsson. Email: vikn@kth.se

First Lecture: The first lecture will be 31/10 10.00-12.00 in U1.

Course Layout: This year the course will be a hybrid between digital and live lectures. The main material will consist of pre-recorded lectures that will be 10-30 min long. We will also have live lectures twice a week where we will do a quick summary of that week's recordings and then go through some examples and exercises.

The recordings can be found at KTH.play here: https://play.kth.se/channel/SF2957+2023/638194

There is a schedule published under files where you can find which recordings you are supposed to listen to before each lecture.

Course literature: Lecture notes and extracts from various books

  • Casella, G. and Berger, R.L., Statistical inference, 2nd Ed., Duxbury Thomson Publishing, 2002.
  • Goodfellow, I., Bengio, Y., and Courville, A., Deep learning, MIT Press, 2016. Available electronically at: https://www.deeplearningbook.org
  • Mackay, D.,  Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. Available as PDF at: http://www.inference.org.uk/itprnn/book.pdf
  • Rasmussen, C.E. and Williams, K.I., Gaussian processes for machine learning, MIT Press, 2006. Available as PDF at: http://www.gaussianprocess.org/gpml/chapters/RW.pdf
  • Sutton, R.S. and Barto, A.G., Reinforcement learning: An introduction, MIT Press, 2017. Available as PDF at:  http://incompleteideas.net/book/bookdraft2017nov5.pdf
  • Kushner, H. J. and Yin, G. G., Stochastic Approximation and Recursive Algorithms and Applications, 2nd Ed., Springer, 2003. 
  • Schervish, M. J., Theory of Statistics, Springer, 1995

Learning outcomes: This course presents an overview of advanced methods of statistical machine learning. The course addresses both theoretical and practical aspects of statistical machine learning. Computer-aided projects with a variety of datasets form an essential learning activity. To complete the course, the student must be able to:

  • formulate and apply statistical decision theory
  • formulate and apply advanced methods in statistical machine learning
  • design and implement advanced methods in statistical machine learning for applications

Examination: The examination consists of projects (3.5 credits) and a final exam (4 credits).  For a passing grade it is required to obtain a pass on the projects as well as the final exam. Registration for the final exam is compulsory. You can register at MyPages. Grades are given in the range A-F, and Fx. The grade Fx gives you the right to a complementary examination to reach the grade E. The criteria for Fx is F and an isolated part of the course can be identified where you have shown a particular lack of knowledge and the examination after a complementary examination on this part can give the grade E. 

Grading: The requirements for higher grades are as follows. 

Learning outcome

E

C

A

Formulate and apply statistical decision theory

Formulate concepts in statistical decision theory and apply the concepts to  solve problems

Motivate methods in statistical decision theory and their properties

Generalise concepts in statistical decision theory and derive fundamental relations

Formulate and apply advanced methods in statistical machine learning

Formulate advanced methods in statistical machine learning and apply the methods to solve problems

Motivate advanced methods in statistical machine learning and their properties

Generalise advanced methods in statistical machine learning and derive fundamental relations

Design and implement advanced methods in statistical machine learning for applications

Ability to design advanced methods in statistical machine learning, implement the methods by writing computer programs to solve applied problems and present, in writing, the methods, motivation of the methods, and results.  

Applies to grade E only. 

 

 

 

 

 

Projects: The projects examine the third learning outcome. Project information will be available on the course's Canvas page. Participation in a project group and writing a report is  mandatory to pass the course. 

•You must work in groups of at most four people.

•You must work actively on the project together with the other group members and be prepared to account for your own and other group members’ contribution.

•You must hand in the report by uploading it on canvas before the deadline.

•You must, together with your project group, be prepared to make a  presentation of your project or part of your project.

Homework: There will be one hand-in assignment on the statistical inference part of the course. The homework can give a maximum of 3 bonus points for the first part of the exam.

Final Exam: The final exam takes place on Jan 9, 2023, 08:00-13:00. Registration for the final exam is compulsory. You can register at MyPages.

The final exam consists of three parts. Part I consists of two problems and examines the first learning outcome. Part II consists of two problems and examines the second learning outcome. Part III consists of one problem and examines higher level learning outcomes. 

Support for students with disabilities: Information about support for students with disabilities (Funka) can be found here. 08-790 6911Länk

Course summary:

Date Details Due