Study year 1

Master's Programme, Machine Learning, 120 credits (TMAIM), Programme syllabus for studies starting in autumn 2015

The following courses are part of study year one.

The course application codes and study periods are valid for the academic year 2015/2016. For other academic years, different application codes and study periods may apply.

General

 Info: DD2301 The Ethical and Successful Machine Learning, 3 credits (2 credits in year 1 and 1 credit in year 2).

A.1.2. Common Elective Courses

Selected freely from all Second cycle courses and language courses given at KTH. First cycle courses at KTH may be taken upon permission from the Programme Director. Not more than 30 ECTS credits in total can be acquired from First cycle courses.

A.2 Conditionally Elective Courses - Application Domains

A student must take at least 4 courses from the grouping listed in A.2 and at least 2 courses from the grouping of courses in A.3 and A.4.

In year 2 can also track Natural Language Processing followed, with the course DD2418.

COMPUTER VISION:
DD2423 Image Analysis and Computer Vision, 7,5
DD2427 Image Based Recognition and Calssification,6.

SPEECH:
DT2112 Speech Technology 7,5
DT2118 Speech and Speaker Recognition 7,5.

VISUALIZATION:
DD2257 Visualization 7,5.

ROBOTICS:
DD2438 Artificial Intelligence and Multi Agent Systems 15.

DATABASES/INFORMATION RETRIEVAL:
DD1368 Database Technology 6
DD2476 Search Engines and Information Retrieval Systems 9
DD2471 Modern Database Systems and Their Applications 7,5.

COMPUTATIONAL BIOLOGY:
DD2435 Mathematical Modelling of Biological Systems 9
DD2401 Neuroscience 7,5
DD2404 Applied bioinformatics. 7.5 credits

A.3 Conditionally Elective Courses - Theory

MATHEMATICS:
EL2320 Applied Estimation 7,5
SF1811 Optimization 6.
STATISTICS & PROBABILITY:
DD2447 Statistical Methods in Applied Computer Science 6
SF2950 Applied Mathematical Statistics 7,5
SF2945 Time Series Analysis 6.

MACHINE LEARNING:
EQ2340 Pattern Recognition 7,5
DD2432 Artificial Neural Networks and Other Learning Systems 6.

A.4 Conditionally Elective Courses - Computer Science

PARALLEL COMPUTING:
SF2568 Parallel Computations for Large- Scale Problems 6.

THEORY:
DD2352 Algorithms and Complexity 7,5.

In study year 2 can the track Software Engineering followed, with the course DD1387 and also the track Security, with courses: DD2395 and DD2448.

Mandatory Appl.code Scope Study period
 1   2   3   4 
DD2380 Artificial Intelligence 6.0 hp 6.0
DD2431 Machine Learning 6.0 hp 6.0
DA2205 Introduction to the Philosophy of Science and Research Methodology
Can be taken in year 2.
7.5 hp 3.0 4.5
DD2301 Program Integrating Course in Machine Learning 3.0 hp 0.5 0.5 0.5 0.5
DD2434 Machine Learning, Advanced Course 7.5 hp 7.5

Conditionally elective Appl.code Scope Study period
 1   2   3   4 
EQ2340 Pattern Recognition
Can be read in study year 2.
7.5 hp 7.5
DD2435 Mathematical Modelling of Biological Systems 9.0 hp 6.0 3.0
DD2423 Image Analysis and Computer Vision
Can be read in study year 2.
7.5 hp 7.5
DD2447 Statistical Methods in Applied Computer Science
Can be read in study year 2.
6.0 hp 6.0
EL2320 Applied Estimation
Can be read in study year 2.
7.5 hp 7.5
SF1811 Optimization 6.0 hp 6.0
DD2432 Artificial Neural Networks and Other Learning Systems 6.0 hp 6.0
DT2112 Speech Technology 7.5 hp 7.5
DD1368 Database Technology 6.0 hp 4.0 2.0
DD2352 Algorithms and Complexity 7.5 hp 4.5 3.0
DD2438 Artificial Intelligence and Multi Agent Systems 15.0 hp 7.0 8.0
DD2476 Search Engines and Information Retrieval Systems 9.0 hp 6.0 3.0
SF2568 Parallel Computations for Large- Scale Problems 7.5 hp 3.0 4.5
DD2257 Visualization 7.5 hp 7.5
DD2401 Neuroscience 7.5 hp 7.5
DD2427 Image Based Recognition and Classification 6.0 hp 6.0
DD2471 Modern Database Systems and Their Applications 7.5 hp 7.5
DT2118 Speech and Speaker Recognition 7.5 hp 7.5
DD2404 Applied Bioinformatics 7.5 hp
SF2945 Time Series Analysis 6.0 hp
SF2950 Applied Mathematical Statistics 7.5 hp