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Syllabus for

Academic year
TDA233 - Algorithms for machine learning and inference
Algoritmer för maskininlärning och slutledning
 
Syllabus adopted 2020-02-11 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING


Teaching language: English
Application code: 02135
Open for exchange students: No
Block schedule: D+
Maximum participants: 120
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0120 Written and oral assignments 3,0c Grading: TH   3,0c    
0220 Examination 4,5c Grading: TH   4,5c    

In programs

MPALG COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (elective)
MPCSN COMPUTER SYSTEMS AND NETWORKS, MSC PROGR, Year 1 (elective)
MPHPC HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)

Examiner:

Not specified

  Go to Course Homepage


Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

To be eligible to the course, the student should have a bachelor degree.
-7.5 credits of programming (Python experience desirable but not absolutely required)
-7.5 credits of data structures or basic algorithm course
-7.5 credits of basic probability and statistics
-7.5 credits of linear algebra
-7.5 credits of calculus 
The course TDA233 cannot be included in a degree which contains (or is based on another degree which contains) the course DAT340.

Aim

This course will discuss the theory and application of basic algorithms for machine learning and inference, from an AI perspective. In this context, we consider learning to draw conclusions from given data or experience which results in some model that generalizes these data. Inference is to compute the desired answers or actions based on the model.
Algorithms of this kind are commonly used in for example classification tasks (character recognition, or to predict if a new customer is creditworthy, etc.) and in expert systems (for example for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.
The course intends to give a good understanding of this cross-disciplinary area, with sufficient depth to use and evaluate the available methods and to understand the scientific literature.

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding:
  • explain a representative set of available methods for machine learning
Competence and skills:
  • implement and analyze machine learning algorithms
  • apply sound mathematical principles to the inference of hypotheses from empirical data and models on scientific grounds
Judgement and approach:
  • choose appropriate methods and apply them to specific inference problems, based on a solid understanding of scientific literature in the field
  • evaluate the methods qualitatively and quantitatively, and recognize their strengths as well as their limitations

Content

The following concepts are covered:
  • Supervised Learning: Bayes classifier, Perceptron, Support vector machines, K-nearest neighbor models, Regression, logistic regression;
  • Maximum likelhood estimation and Bayesian methods;
  • Unsupervised Learning: Clustering algorithms, EM algorithm, Mixture models, Model selection, Kernel methods;
  • Deep Learning models such as fully connected neural networks, Convolutional Neural Networks, Recurrent Neural Networks.

Organisation

Lectures and homework assignments.

Literature

See course homepage.

Examination including compulsory elements

Assignments and written examination.

The grading scale comprises: U, 3, 4, and 5. A passing grade for the entire course requires at least a passing grade for all sub-courses.
To be awarded a higher passing grade for a full course, the student must, in addition, receive a higher passing grade on the sub-course written hall examination.


Published: Mon 28 Nov 2016.