1 lecture 1 introduction to the master’s programme ”statistics and data mining” practical...
TRANSCRIPT
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Lecture 1
Introduction to the Master’s Programme
”Statistics and Data Mining”
Practical questions
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Personnel at Statistics and Machine Learning department
Introductory course "Statistics and Data Mining"
Name Name
Oleg Sysoev
Senior lecturer
Responsible for “Statistics and Data Mining”
Mattias Villani
Professor
Division chief
Anders Nordgaard
Senior lecturer
Ann-Charlotte Hallberg(Lotta)
Director of studies
Lecturer
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Personnel at Statistics and Machine Learning department
Introductory course "Statistics and Data Mining"
Name Name
Bertil Wegmann
Postdoc
Per Sidén
PhD student
Annelie Almquist
Administrator(registration, course reporting,…)
Lilian Alarik
Study councellor
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Personnel at Statistics and Machine Learning department
Introductory course "Statistics and Data Mining"
Name Name
Linda Wänström
Senior lecturer
Måns Magnusson
PhD Student
Karl Wahlin
Senior lecturer
Responsible for the bachelor program
Josef Wilzén
PhD Student
Personnel at ADIT
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Introductory course "Statistics and Data Mining"
Name Name
Patrick Lambrix
Professor
Jose Pena
Senior lecturer
This course
Lectures – Attendance is obligatory Reading one statistical paper and writing a summary Reading one more statistical paper and writing a critical
review URKUND is used Plagiarism is forbidden! (discovered
plagiarism implies a request to disciplinary board) Course end: January 2016 Grading for this course: Pass fail Several teachers from IDA are involved You meet other IDA master students
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Introductory course "Statistics and Data Mining"
Statistics and Data Mining program
Aims: To build advanced models for explaining complex real-
life systems and predicting new events To extract, organize and explore large volumes of data To learn how to discover important (hidden) information
(trends, patterns) from large and complex data sets To get an in-deep knowledge of models and methods
Competences: Data mining, machine learning, statistical modeling,
visualization methods, databases, programming etc
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Introductory course "Statistics and Data Mining"
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Introductory course "Statistics and Data Mining"
Job opportunities
A plenty of jobs are waiting in USA and Europe Master program gives excellent background to search jobs as
analyst, engineer, manager or consultant in Business Intelligence (BI) Customer Resource Management (CRM) Bioinformatics Economics IT industry
…and many other areas where large or complex datasets are involved
Example jobs: Predictive Modeling and Data Mining Scientists/Analysts, USA Statistical Modeller/Software Developer, London Analytiker, Försäkringskassan
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Introductory course "Statistics and Data Mining"
Master program overview Master program= 120 ECTS credits
Obligatory courses (42 ECTS) You must take and finish these courses to get a degree
Introductory courses (at least 6 ECTS) Advanced R programming: recommended for all students missing a solid
programming background Statistical methods: recommended for people with a little statistics in the
background, i.e. computer scientists or engineers (check syllabus if you are not sure)!
Profile courses (at least 12 ECTS) Those are courses in statistics that you need to take in order to get a degree
in Statistics. Complementary courses
If you have found some interesting course which is not in the schedule, we may count it as profile, contact Oleg S.
Master thesis (30 ECTS)
In order to make a sufficient progress in studies, you need to gain 30 ECTS credits/ semester
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Introductory course "Statistics and Data Mining"
Semester admission rules
at least 6 ECTS credits of the first semester to be admitted to the second semester
at least 40 ECTS credits of the first year, in order to be admitted to the third semester
65 ECTS credits of the programme, including all obligatory courses, in order to be admitted to the master thesis course.
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Introductory course "Statistics and Data Mining"
Master program overview
12Introductory course "Statistics and Data Mining"
Year 1Semester 1 Semester 2
Period 1 Period 2 Period 3 Period 4Advanced Academic Studies
(732A42, 3 credits)Data mining - clustering and
association analysis(732A31, 15 credits)
Philosophy of science (720A04
,3 credits)
Time series analysis(732A34, 6 credits)
Introduction to Machine Learning(732A52, 9 credits)
Computational statistics(732A38, 6 credits)
Bayesian learning,(732A46, 6 credits)
Advanced R programming(732A50, 6 credits)
Neural Networks and Learning Systems
(TBMI26, 6 credits)
Multivariate statistical methods
(732A37, 6 credits)Statistical methods ( 732A49, 6 credits)
Web programming and interactivity
(TDDD24, 4 credits)
Master program overview
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Introductory course "Statistics and Data Mining"
Year 2
Semester 3 Semester 4
Period 1 Period 2 Period 3 Period 4Visualization
(732A39, 6 credits)
Advanced Machine learning(732A37, 6 credits)
MASTER THESIS
(732A30, 30 credits)Optimization(TAOP23, 6 credits)
Text Mining(732A47, 6 credits)
Probability theory(732A40, 6 credits)
Database Technology(TDDD37 , 6 credits)
Data mining project (732A32, 6 credits)
Statistical evidence evaluation (732A45, 6 credits)
EXCHANGE STUDIES
Obligatory courses
Academic studies (several sessions, ends before january) Introduction to Machine Learning
Predictive modelling: Ridge regression, Generalized additive models, neural networks, support vector machines etc
Data Mining – Clustering and Association analysis Unsupervised learning, focus on algorithms
Philosophy of science Laws of nature and scientific models, theories and observations
Computational statistics Random number generation, MCMC
Bayesian learning Using prior knowledge to make better decisions and inferences
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Introductory course "Statistics and Data Mining"
Introductory courses
Statistical Methods Probability, conventional distributions: Normal, Poisson,
Gamma… Point and interval estimation Hypothesis testing Basics of Bayesian statistics
Advanced programming in R Basic programming (loops, data types) Advanced topics (debugging, peformance enhancement
etc).
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Introductory course "Statistics and Data Mining"
Profile courses
Visualization Static, interactive and dynamic graphics for data analysis
Time Series Analysis Autocorrelation, forecasting, ARIMA models
Probability theory Multivariate random variables, transforms, order statistics,
convergence. Necessary for PhD studies. Multivariate statistical methods
Principal components, factor analysis, canonical correlation Statistical evidence evaluation
Methods to secure, analyze and interpret (technical) evidence to be used in the legal process of particular cases
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Introductory course "Statistics and Data Mining"
Profile courses
Neural networks and learning systems Given by Department of Biomedical Engineering Advanced
neural networks, kernel methods, reinforcement learning, genetic algorithms
Web programming course HTML, XML, PHP
Optimization Linear, nonlinear, network optimization
Data mining project Specify, implement and evaluate a data mining algorithm
Text Mining Extracting text data from different sources and analysis
linguistically and by statistical tools Database technology
Relational databases, relational algebra, SQL, query optimization
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Introductory course "Statistics and Data Mining"
Other information
Master program’s homepage(schedule, courses, news…):http://www.liu.se/en/education/master/programmes/F7MSM/student?l=en
Facebook page:https://www.facebook.com/liustatisticsmaster
Email to staff: [email protected] Example: [email protected]
Webpages of courses: www.ida.liu.se/~course_code/ This course: www.ida.liu.se/~732A42/
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Introductory course "Statistics and Data Mining"
Course registration To get credits for a course, you must register on it. International students: register for max 120 ECTS, you pay for more
than that! (Swedish language courses not included) Registration is done by Student Portal:
https://www3.student.liu.se/portal/eng If you have problems with registration, contact our administrator
Annelie Almquist ([email protected] )
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Introductory course "Statistics and Data Mining"
Here you can choose between registration for single subject course or study program
LiU-Account and personal number
It is necessary for you to get a LIU-account as soon as possible (house Zenit, student office) Access to Student Portal Course registration Access to course materials Access to department computers
If you come outside Sweden, it is very important to get a Personal Number at the Tax office: Address: Kungsgatan 37, Linköping Needed for medical help
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Introductory course "Statistics and Data Mining"
Lectures, Labs, Seminars
Lectures: normally presented in PowerPoint, later available either at course page or LISAM. Attendance is typically not obligatory
Labs: typically computer exercises done individually or in groups of two. Attendance is typically not obligatory. A written report should be normally submitted.
Seminars: Discussions of theory and labs, student presentations. Attendance typically obligatory.
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Introductory course "Statistics and Data Mining"
Exams and points
Exams Each course has 1 exam and 2 re-exams You must register for the written or computer exam at
least 10 days in advance. Exam results may not be improved if aim for high grade
and feel that written badly cross every non-empty page in solutions
Exam results should normally be available after 2 weeks Credits
Some courses have separate credits for labs (or project) and for the exam
Credits for some courses can be obtained only after you are completely done with the course
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Introductory course "Statistics and Data Mining"
Course evaluation
KURT: course evaluation system at LiU You evaluate the courses you have done Sent via email The surveys are anonymous! Very important for improvements of courses – please
answer these surveys! You will be invited to a meeting with study councellor
periodically to discuss your current studies and plan the coming studies.
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Introductory course "Statistics and Data Mining"
Schedules of the courses
Some schedules are on the course homepages
Some schedules accessed via TimeEdit: https://se.timeedit.net/web/liu/db1/schema
Type the course name and run
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Introductory course "Statistics and Data Mining"
How to find a room
Room at LiU: http://www.liu.se/karta/?l=en
Room at the department (IDA) Go to www.ida.liu.se and choose “Find IDA room” from the
droplist.
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Introductory course "Statistics and Data Mining"
Useful links
Homepage "Statistics and Data Mining" Information from the faculty Practical Guide Welcome activities for masters
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Introductory course "Statistics and Data Mining"
Questions
Questions related to the program? Contact Oleg Sysoev
http://www.ida.liu.se/department/contact/contactsearch.en.shtml?NAME=Oleg%20Sysoev
Questions about master studies in general? Contact Darja Utgof
http://www.student.liu.se/masters/master-s-coordinators?l=en
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Introductory course "Statistics and Data Mining"