Welcome!

This is the web site for the thesis course in the master's programme Statistics and Machine Learning.
We have posted some useful information and material below. This is the only course webpage to keep track of during this course.

Timetable for the Spring 2025 course

Date	Activity	Material
October 9, 2024	Preparatory meeting at 10.15 in KY23, Key-house
December 20, 2024	Deadline for sending in project descriptions to Oleg.
January 9, 2025	Approval of the thesis topic
January 16	Appointment of the supervisor
January 17	Introductory meeting. At 15.15 in Alan Turing, E-house	Slides
January 31	Submitting thesis proposal presentation to LISAM
February 5-6	Thesis proposal seminar in Alan Turing (group A) and John von Neumann (group B).	Schedule for group A Schedule for group B
March 13	Submitting the midterm presentation plus progress report plus thesis text sample to LISAM
March 20-21	Mid-term seminar in A33, A-house (March 20, group A) and Alan Turing (March 21, group A) and John von Neumann (group B, both days).	Schedule for group A Schedule for group B
April 24	Sending a thesis draft to the supervisor by email unless the supervisor states otherwise.
April 30	Decision is made by the supervisor regarding whether the thesis has good chances to be passed within the course time frames or the thesis work needs to be stopped and grade F given.
May 4	Sending a complete thesis draft (it may not contain any missing part) to the opponent, examiner and supervisor by email
May 12 or 13 or 14	Revision meeting. Mandatory, but date and place are set bilaterally selected pairs of students.
May 25	Submission of the close-to-final thesis to LISAM. This version will be controlled for the plagiarism. Send it also to the supervisor, examiner and opponent by email
June 2	Submission of the presentation for the oral defense to LISAM
June 3 - June 5	Oral defense seminar in Alan Turing (group A) and John von Neumann (group B).
June 9	Last day to submit final version of the thesis for it to be reported within this semester
June 17	Examiner's meeting where the grades are decided and then reported
August, 4	Last attempt for submitting the final version of the thesis (for students who passed all obligatory moments except of June 9 submission; the maximum possible grade is C)
January, 2026	Second opportunity for the oral defense seminar (for students who passed all obligatory moments except of  defense)

Bold means that the entire meeting is mandatory. If a group is indicated, the entire meeting is mandatory for students from this group.
Note that LiU makes use of academic quarters.
All deadlines specified refer to the latest time point 23:59.

Timetable for the Autumn 2024 course

Date	Activity	Material
August 9	Deadline for sending in project descriptions to Frank.
August 14	Approval of thesis topic.
August 20 or earlier	Appointment of supervisor.
August 21	Introductory meeting at 10.15 in John von Neumann, B building.	Slides
September 9	Submitting the thesis proposal presentation to LISAM.
September 13	Thesis proposal seminar. In John von Neumann, B building.	Schedule
October 18	Submitting the midterm presentation and supporting report to LISAM.
October 24	Mid-term seminar.  In John von Neumann, B building.	Schedule
November 22	Sending a thesis draft to the supervisor unless the supervisor states otherwise.
November 29	Decision is made by the supervisor regarding whether the thesis has good chances to be passed within the course time frames or the thesis work needs to be stopped and grade F given.
December 4	Sending a thesis draft to the opponent, examiner and supervisor.
December 11, 12 or 13	Revision meeting. Mandatory, but date and place are decided in agreement between the student, opponent, supervisor and examiner.	Opponents for theses
January 5, 2025	Submission of the close-to-final thesis to LISAM. This version will be controlled for the plagiarism. Send it also to the supervisor, examiner and opponent by email.
January 12, 2025	Submission of the presentation for the oral defense to LISAM.
January 13, 2025	Oral defense seminar. In John von Neumann, B building.	Schedule
January 20, 2025	Last day to submit final version of the thesis for it to be reported within this semester.
January 27, 2025	Examiner's meeting where the grades are decided and then reported.
March 21, 2025	Last attempt for submitting the final version of the thesis (for students who become delayed; the maximum possible grade is C).
June, 2025	Second opportunity for the oral defense seminar (for students who become delayed).

Letter to company collaborators

- Letter

Proposed projects start Spring 2025

Internal, at IDA:

Link for projects in Spring 2025

Templates and instructions for the report

For the templates, and the publication rules, see here

Examples of theses from previous years (grade A or B)

2020-2022

Can be found here

Older theses:

2017

Caroline Svahn, Automated Bug Report Routing

Andrea Bruzzone,P-SGLD: Stochastic Gradient Langevin Dynamics with control variates

Aleksey Shipitsyn, Statistical Learning with Imbalanced Data

2016

Alessandro Olivi, Survival analysis of gas turbine components

Malte Nalenz, Horseshoe RuleFit - Learning Rule Ensembles via Bayesian Regularization

Konstantinos Poulakis, Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques

Claudia Adok, Retrieval of Cloud Top Pressure

Miriam Hurtado Bodell, Bayesian poll of polls for multi-party systems

2015

Patrik Pavlov, Optimal Experimental Design and Parameter Estimation of a Stacked Bed Hydrotreating Process

Martin Arvidsson, Dynamic Call Drop Analysis

Andreea Bocancea, Supervised Classification Leveraging Refined Unlabeled Data

2014

Aiswaryaa Viswanathan, Data driven analysis of usage and driving parameters that affect fuel consumption of heavy vehicles

Munezero Parfait, Mobile network traffic analysis based on IP log data

Uriel Chareca, Inferring user demographics from reading habits

Paolo Elena, Anomaly detection and analysis on dropped phone call data

2013

Lotta J rvstr t, Functionality Classification Filter for Websites

2012

Ankita Garg, Forecasting exchage rates using machine learning models with time-varying volatility