Undergraduate Course: Informatics 2B - Learning (INFR08028)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides an introduction to some of the basic mathematical and computational methods for learning from data. We discuss the problems of clustering and classification, and how probabilistic and non-probabilistic methods can be applied to these.
This course replaces Informatics 2B - Algorithms, Data Structures, Learning (INFR08009) for 2019/20. |
| Course description |
Course syllabus:
* Statistical pattern recognition and machine learning
* Multidimensional data
* Discrete data and naive Bayes
* Modelling and describing continuous data: nearest
neighbours and clustering
* Gaussians and linear discriminants
* Single- and multi-layer networks
|
Information for Visiting Students
| Pre-requisites | Background required: at least one semester of programming; linear algebra; calculus (differentiation). |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2019/20, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
75 )
|
| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam _75%
Practical Exam _____% (for courses with programming exams)
Coursework _25%
One assessed assignment. You should expect to spend around 25 hours on the assignment. |
| Feedback |
Solutions and strategies for bi-weekly exercise sets will be discussed in tutorial groups, with an opportunity for students to ask questions. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | | | Resit Exam Diet (August) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Manipulate and describe multidimensional data using summary statistics.
- Define and use methods such as Naïve Bayes, Gaussians, and single-layer networks to model and classify multidimensional data.
- Describe and apply nearest-neighbour and clustering approaches and the concept of discrimi-nant functions.
|
Reading List
| Course notes will be provided. |
Additional Information
| Graduate Attributes and Skills |
Problem-solving, analytical thinking, numeracy. |
| Special Arrangements |
A background in calculus (differentiation of simple functions) is also required.
INF1-Introduction to Computation (INFR08025) replaces INF1-Computation and Logic (INFR08012) and INF1-Functional Programming (INFR08013) from 2018/19. |
| Keywords | Machine learning,data science |
Contacts
| Course organiser | Dr Hiroshi Shimodaira
Tel: (0131 6)51 3279
Email: |
Course secretary | Ms Kendal Reid
Tel: (0131 6)51 3249
Email: |
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