Hide menu

732A64 Master's Thesis

Course information

Welcome!

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



Preliminary Timetable for the Autumn 2020 course

Date Activity Material
August 2
Deadline for sending in project descriptions to Oleg.
August 10
Approval of thesis topic

August 21 or earlier Appointment of supervisor.
August 24 Introductory meeting. At 10-12 in Alan Turing
Slides
September 6 Submitting the thesis proposal presentation to LISAM
September 10
Thesis proposal seminar. Where? Will be announced later

October 26 Submitting the midterm presentation and supporting report to LISAM
October 30
Mid-term seminar.  Where? Will be announced later

November 26 Sending a thesis draft to the supervisor unless the supervisor states otherwise.
December 3 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 8 Sending a thesis draft to the opponent, examiner and supervisor
December 16, 17 or 18 Revision meeting. Mandatory, but date and place are set bilaterally selected pairs of students.
January 8, 2021
Submission of the close-to-final thesis to LISAM and to Oleg's URKUND address.
Send it also to the supervisor, examiner and opponent by email

January 13,2021 Submission of the presentation for the oral defense to LISAM
January 14, 2021 Oral defense seminar. Where? Will be announced later
January 20, 2021
Last day to submit final version of the thesis for it to be reported within this semester
January 27, 2021 Examiner's meeting where the grades are decided and then reported
March 20, 2021 Last attempt for submitting the final version of the thesis
(for students who become delayed; the maximum possible grade is C)

June, 2021 Second opportunity for the oral defense seminar (for students who become delayed)

Bold means that the meeting is mandatory.
Note that LiU makes use of academic quarters.
All deadlines specified refer to the latest time point 23:59.

Timetable for the Spring 2020 course

Date Activity Material
October 1, 2019
Preparatory meeting at 13-15 in Alan Turing, E-house
Slides
December 13, 2019
Deadline for sending in project descriptions to Oleg.
December 20, 2019
Approval of thesis topic

January 17 Appointment of supervisor. Project titles, supervisors, examiners and groups
January 20 Introductory meeting. At 10-12 in Alan Turing
Slides
February 5 Submitting the thesis proposal presentation to LISAM
February 10-11
Thesis proposal seminar
February 10: Group A. Where? See TimeEdit
February 11: Group B.
Where? See TimeEdit
Schedule for group A
Schedule for group B
March 27 Submitting the midterm presentation to LISAM
April 1-2
Mid-term seminar
April 1: Group A. Where? See TimeEdit
April 2: Group B.
Where? See TimeEdit
Schedule for group A
Schedule for group B
April 30 Sending a thesis draft to the supervisor unless the supervisor states otherwise.
May 6 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 8 Sending a thesis draft to the opponent, examiner and supervisor See the opponent list in the schedule for the oral defense
May 15 or 18 or 19
Revision meeting. Mandatory, but date and place are set bilaterally selected pairs of students.
May 26 Submission of the close-to-final thesis to LISAM and to Oleg's URKUND address. Send it also to the supervisor, examiner and opponent by email

May 31 Submission of the presentation for the oral defense to LISAM
June 1-3 Oral defense seminar
June 1 (8.00-17.20)+ June 2 (8.00-13.00): Group A. Where? See TimeEdit
June 2 (13.00-17.20)+ June 3 (8.00-17.20): Group B.
Where? See TimeEdit
Schedule for group A
Schedule for group B

June 8 Last day to submit final version of the thesis for it to be reported within this semester

June 12 Examiner's meeting where the grades are decided and then reported
August, 7 Last attempt for submitting the final version of the thesis
(for students who become delayed; the maximum possible grade is C)

January, 2021 Second opportunity for the oral defense seminar (for students who become delayed)

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.


Letter to company collaborators

- Letter




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)

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 Jrvstrt, Functionality Classification Filter for Websites

2012

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


Page responsible: Mattias Villani
Last updated: 2020-05-26