Syllabus for |
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| TMS101 - Basics in mathematical statistics and computer science |
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| Owner: BIMAS |
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7,0 Credits (ECTS 10,5) |
| Grading: TH - Five, Four, Three, Not passed |
| Level: A |
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Department: 11 - MATHEMATICAL SCIENCES
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Teaching language: English
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
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No Sp |
| 0103 |
Examination, part A |
3,5 c |
Grading: TH |
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3,5 c
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18 Oct 2004 pm V, |
08 Jan 2005 am V |
| 0203 |
Examination, part B |
3,5 c |
Grading: TH |
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3,5 c
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21 Oct 2004 am V 
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15 Jan 2005 am V |
In programs
BIMAS MSc PROGRAMME IN BIOINFORMATICS, Year 1 (elective)
Examiner:
Professor
Serik Sagitov
Replaces
TMS100
Basics in mathematical statistics and computer science
Eligibility:
For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.
Course specific prerequisites
Introductory programming course.
Aim
The aim of this course is to provide some basic ideas, tools and techniques in mathematics, statistics and computer science. The course is intended for students with limited previous experiences of these areas, and it is specially designed to give the necessary mathematical prerequisites to follow later courses in the Master's programme in Bioinformatics.
Content
Part 1: Data structures and algorithms.
This part of the course mainly considers data structures and algorithms, which in addition to basic programming are necessary for understanding algorithms and successfully implementing programs in any area of application. In addition to basic knowledge, the course emphasises the skills needed to independently analyse and solve algorithmic problems.
Content: Data structures and abstract data types. Common data structures such as lists, trees and graphs, both with respect to their abstract properties and their implementation. Common algorithms related to these data structures and basic analysis of their time and memory requirements. Algorithms for basic problems such as sorting, shortest path and minimal spanning tree.
Part 2: Mathematical statistics.
Experimental research in the sciences and in engineering involves the use of experimental data, a sample, from which to draw conclusions about the nature of a phenomenon under study. However, inference based on sampled data will always be subject to uncertainty; the information provided by one sample depends on the particular sample chosen and will thus change from sample to sample. Statistics, sometimes called the science of data, includes methods to evaluate the reliability of conclusions based on data.
Content: Combinatorics. Probability. Conditional probability and independence. Random variables and some common probability distributions. Expectation. The central limit theorem. Maximum likelihood estimation. Hypothesis testing.
Organisation
The course is organised with lectures and practically oriented homework and programming exercises.
Literature
Preliminary literature:
Part 1: Weiss: Data structures and algorithm analysis in Java.
Part 2: Rice, J.A. Mathematical Statistics and Data Analysis. International Thomson Publishing, 1995.
Examination
Part 1 and 2: Exercises and a written exam.