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732A95 Introduction to Machine Learning

Course information

Course description

The course introduces the main concepts and tools in machine learning which are necessary for professional work and research in data analytics. The course presents machine learning mainly from a probabilistic framework, but successful non-probabilistic methods are also covered.

The course is given in the following formats:

  • Master level course 732A95: the students are supposed to participate in the sessions from block 1 and 2. Obligatory attendance of the seminars. Examination by submission of the lab reports and a computer-based exam
  • Engineering course TDDE01: the students are supposed to participate in the sessions  from block 1. Obligatory attendance of the seminars. Examination by submission of the lab reports and a computer-based exam
  • PhD course: the students are supposed to participate in the sessions from block 1 and 2. Examination by submission of the lab reports.

The course is divided into topics, where each topic includes lectures, a computer lab and a follow-up seminar.




Course literature

- Pattern recognition and machine learning (PRML) by C.M. Bishop, ISBN 9780387310732.
- The Elements of Statistical Learning (ESL) by T. Hastie, R. Tibshirani and J. Friedman, Second Edition, ISBN 9780387848587, available for free here.
- Slides
- Other material distributed during the course.



BLOCK 1: given for TDDE01/732A95/PhD students

Topic 1: Basic concepts in machine learning. Software. Regression, regularization and model selection.

Teacher: Oleg Sysoev.
Read: ESL 1, 2, Ch. 3.1-3.4.3, Ch 7. PRML 1.1-1.5, 2.1-2.2, 2.3.6, 2.5,  3.1, 1.5, 3.2 and 3.4. You may also find this wikipedia page on ROC curves helpful.
Lecture 1a: Introduction to Machine Learning. Slides in LISAM
Lecture 1b:Introduction to R. Slides in LISAM
Lecture 1c: Introduction to Bayesian methods. Slides in LISAM
Lecture 1d: Slides in LISAM
Lecture 1e: Slides in LISAM


Topic 2: Classification methods. Dimensionality reduction and uncertainty estimation.

Teacher: Oleg Sysoev.
Read: a) PRML ch. 2.4-2.4.1, 4.2-4.3, 12.1-12.2.1 ESL ch 4.3, 4.4, 14.7, 3.5 and paper “Bootstrap confidence intervals: when, which, what?” by Carpenter and Bithell
b) https://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf and ESL, ch. 9.2
Lecture 2a: Slides in LISAM
Lecture 2b: Slides in LISAM
Lecture 2c: Slides in LISAM
Lecture 2d: Slides in LISAM


Topic 3: Kernel methods and support vector machines. Neural networks and deep learning.

Teacher: Jose M. Peña.
Read: a) PRML ch. 2.5 and 6.1-6.2, ch. 7.1,ch. 5.1-5.3.3, 5.5.3 and 5.5.6 and ESL ch. 6 
Lecture 3a: Slides in LISAM
Lecture 3b: Slides in LISAM
Lecture 3c: Slides in LISAM
Lecture 3d: Slides in LISAM



BLOCK 2: given for 732A95/PhD students


Topic 1: Ensemble methods and mixture models. Online Learning.

Teacher: Jose M. Peña.
Read: a) ESL ch. 10.1-10.10 and 15.1-15.4 and PRML ch. 14.1-14.4 b) PRML ch. 2.3.9, 9.1-9.3.3 and 14.5.3 and ESL ch. 8.5. See slides also.
Lecture 1a: Slides in LISAM
Lecture 1b: Slides in LISAM
Lecture 1c: Slides in LISAM


Topic 2: Splines and additive models. High-dimensional problems.

Teacher: Oleg Sysoev.
Read:  ESL  Ch. 5.1-5.5, 5.7, 9.1, 18.1-18.4 and 18.6
Lecture 2a: Slides in LISAM
Lecture 2b: Slides in LISAM




Page responsible: Oleg Sysoev
Last updated: 2017-08-23