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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Informatics 2B - Learning (INFR08028)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Informatics 1 - Computation and Logic (INFR08012) AND Informatics 1 - Functional Programming (INFR08013) OR Informatics 1 - Introduction to Computation (INFR08025)) AND
Students MUST have passed: Informatics 1 - Object-Oriented Programming (INFR08014) AND Introduction to Linear Algebra (MATH08057)
Prohibited Combinations Other requirements 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.
Information for Visiting Students
Pre-requisitesBackground 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:
  1. Manipulate and describe multidimensional data using summary statistics.
  2. Define and use methods such as Na´ve Bayes, Gaussians, and single-layer networks to model and classify multidimensional data.
  3. 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.
KeywordsMachine learning,data science
Course organiserDr Hiroshi Shimodaira
Tel: (0131 6)51 3279
Course secretaryMs Kendal Reid
Tel: (0131 6)51 3249
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