The aim of the course is to introduce some of the basic algorithms and methods of statistical learning theory at an intermediate level. These are essential tools for making sense of the vast and complex data sets (c.f. big data) that have emerged in fields ranging from biology to marketing to astrophysics in the past decades. The course presents some of the most important modeling and prediction techniques, along with some relevant applications. Topics presented include linear regression, classification, Bayesian learning, resampling methods, shrinkage approaches, tree-based methods, and clustering. This is a good part of the background required for a career in data analytics. The course is lectured and examined in English.

Recommended prerequisities:

• SF 1901 or equivalent course of the type 'a first course in probability and statistics (for engineers)'
• Multivariate normal distribution
• Basic differential and integral calculus, basic linear algebra.

Lecturers:

• Timo Koski (examiner) homepage and contact information

• Jimmy Olsson email

• Tetyana Pavlenko email

Course literature::

• G. James, D. Witten, T. Hastie, R. Tibshirani: An introduction to Statistical Learning web page for the book (acronym below: ISL) by the publisher Springer
• Supplementary reading and material from the lectures.

• Computer homework (3.0 cu): there are three compulsory computer projects/home work that are to be submitted as written reports. Each report should be written by a group of two (2) students. The reports are examined at the Project presentation seminars on the x th of November and xth of December, 2016. The computer homework will be graded with Pass/Fail.
• There will be a written exam (4.5 cu), consisting of five (5) assignments, on Friday 15th of January, 2017, xx- 13.00.
  
  
• Bonus for summaries of the guest lectures An individually written summary (max. 2xA4) of the scientific contents of a guest lecture will provide one (1) bonus point for the exam. The summary is expected to be based on the students' own notes taken during the lecture. The summaries must be submitted with deadline Tue 8th of December at 15 hrs. The bonus points are valid for the ordinary Exam on Friday 15th of January, 2017, and in the re-examination on xxx (TBA). The maximum number of bonus points to be gained is five (3).
  
  
  
• Important: Students, who are admitted to a course and who intend to attend it, need to activate themselves in Rapp . Log in there using your KTH-id and click on "activate" (aktivera). The codename for sf2935 in Rapp is statin15.
  Registration for the written examination via "mina sidor"/"my pages" is required.
  Grades are set according to the quality of the written examination. Grades are given in the range A-F, where A is the best and F means failed. Fx means that you have the right to a complementary examination (to reach the grade E). The criteria for Fx is a grade F on the exam, and that an isolated part of the course can be identified where you have shown a particular lack of knowledge and that the examination after a complementary examination on this part can be given the grade E.
  
  

• Supervision for computer projects
  Teaching assistants will be available for advice and supervision for computer projects every Wednesday 16-18 hrs in room 3721 plan 7, Lindstedtsvägen 25 starting on the 11th of November and till 15th of December.
  
  

  Plan of lectures
  
  (TK=Timo Koski, JO= Jimmy Olsson TP=Tetyana Pavlenko, EA= Erik Aurell, JC= Jan Conrad, MG= Michael Gutman, LK= Lukas Käll, JW= Johan Wästerborn, FR= Felix Rios, ISL = the textbook ) 
  The addresses of the lecture halls and guiding instructions are found by clicking on the Hall links below
  
  

  
  

  Day	Date	Time	Hall	Topic	Lecturer
  Mon	02/11	13-15	V12	Lecture 1: Introduction to statistical learning and the course work. Introduction to computer projects Chapter 2 in ISL.	TK, JW
  Tue	03/11	13-15	V12	Lecture 2: Multiple Regression reviewed and recollected Chapter 3 in ISL	TP
  Thu	05/11	08-10	K53	Lecture 3: Supervised Learning Part I. Chapter 4 in ISL	TP
  Mon	09/11	13-15	Baltzar	Introduction to R in a computer class Chapter 2 in ISL	JW, FR
  Tue	10/11	13-15	V12	Lecture 4: Supervised Learning Part II. Chapter 4 in ISL	TP
  Thu	12/11	08-10	V12	Lecture 5: Supervised Learning Part III, (logistic regression), Chapter 4 in ISL, handouts.	TK
  Mon	16/11	13-15	V12	Lecture 6: Bootstrap	JW
  Tue	17/11	13-15	V12	Lecture 7: GUEST LECTURE: An insight into computational and statistical mass spectrometry-based proteomics	LK
  Thu	19/11	13-15	V12	Lecture 8:Bayesian Learning part I. Handout	TK
  Mon	23/11	13-15	V12	Lecture 9: :Bayesian Learning part II. Handout	TK
  Tue	24/11	13-15	V12	Lecture 10: Crossvalidation, Chapter 5 in ISL	TP
  Thu	26/11	08-10	V12	Lecture 11: Linear model selection and regularization part Chapter 5 in ISL	TP
  Mo	30/11	13-15	V12	Lecture 12: Guest Lecture: Inferring protein structures from many protein sequences	EA
  Tue	01/12	13-15	V12	Lecture 13: Guest Lecture Approximate Bayesian Computing	MG
  Thu	03/12	08-10	V12	Lecture 14: Guest Lecture Deep Learning	MG
  Mon	07/12	13-15	Q13	Lecture 15: Guest lecture: Machine Learning in Searches for Particle Dark Matter.	JC
  Tue	08/12	13-15	D35	Lecture 16: Unsupervised learning part I. Chapter 10 in ISL	TK
  Thu	10/12	08-10	Q13	Lecture 17: Unsupervised learning part II. Chapter 10 in ISL	TK
  Mon	14/12	13-15	V12	Lecture 18: Random Trees and Classification. Chapter 8 in ISL	JO
  Tue	15/12	13-15	V12	Project presentation seminar 1	TK, TP
  Thu	17/12	08-10	V12	Project presentation seminar 2	TK, TP
  Fri	15/01/2016	08-13	E31 (TBA)	Exam	TK

  Welcome, we hope you will enjoy the course (and learn (sic) a lot)!

  Tetyana, Jimmy & Timo

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