Mathematical Sciences at Oxford

Stephen Drape

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Who am I?

Dr Stephen Drape

Access and Schools Liaison Officer for Computer Science (Also a Departmental Lecturer)

8 years at Oxford (3 years Maths degree, 4 years Computer Science graduate, 1 year lecturer)

5 years as Secondary School Teacher

Email: stephen.drape@comlab.ox.ac.uk

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Four myths about Oxford

There’s little chance of getting in It’s very expensive in Oxford College choice is very important You have to be very bright

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Myth 1: Little chance of getting in

False! Statistically: you have a 20–40% chance

Admissions data for 2007 entry:Applications Acceptances %

Maths 828 173 20.9%

Maths & Stats 143 29 20.3%

Maths & CS 52 16 30.8%

Comp Sci 82 24 29.3%

Physics 695 170 24.5%

Chemistry 507 190 37.5%

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Myth 2: It’s very expensive

False! Most colleges provide cheap accommodation for three years.

College libraries and dining halls also help you save money.

Increasingly, bursaries help students from poorer backgrounds.

Most colleges and departments are very close to the city centre – low transport costs!

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Myth 3: College Choice Matters

False! If the college you choose is unable to offer you a place because of space constraints, they will pass your application on to a second, computer-allocated college.

Application loads are intelligently redistributed in this way.

Lectures are given centrally by the department as are many classes for courses in later years.

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Myth 3: College Choice Matters

However… Choose a college that you like as you have to live and work there for 3 or 4 years

Look at accommodation & facilities offered. Choose a college that has a tutor in your subject.

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Myth 4: You have to be bright

True! We find it takes special qualities to benefit from the kind of teaching we provide.

So we are looking for the very best in ability and motivation.

A typical offer is 3 A grades at A-Level

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The University

The University consists of: Colleges Departments/Faculties Administration Student Accommodation Facilities such as libraries, sports

grounds

The University is distributed throughout the whole city

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Departments vs Colleges

Departments are responsible for managing each courses by providing lectures, giving classes and setting exams

College can provide accommodation, food, facilities (e.g. libraries, sports grounds) but also gives tutorials and admits students

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Teaching

Teaching consists of a variety of activities:

Lectures: usually given by a department Tutorials: usually given in a college

(often 1 tutor with 2 students) Classes: for more specialised subjects Practicals: for many Science courses Projects/Dissertations: for some courses

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Colleges

There are around 30 colleges in Oxford – some things to consider:

Check what courses each college offers Accommodation Location FacilitiesYou can submit an open application

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Applications Process

Choose a course Choose a college that offers that course Your application goes to a college rather

than the University as a whole since college admissions tutors decide who to admit.

You can choose a first choice college – second and third choices get allocated to you.

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Interviews

Interviews take place over 2 or 3 days. Candidates stay within college Mostly candidates will have interviews at

the first and second choice colleges For some subjects, samples of written

work or interview tests are needed

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What do interviewers assess?

Motivation Future potential Problem solving skills Independent thinking Commitment to the subject

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Common Interview Questions Why choose Oxford?

Candidates often say “Reputation” or “It’s the best!”

Why do you want to study this subject? Frequent response: “I enjoy it”

It’s important to say why the course is right for you – look at the information in the prospectus.

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What tutors will consider

Academic record (previous and predicated grades)

School reference UCAS statement (be careful what you

say!) Written work or entrance test (as

appropriate) Interview performance

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Mathematical Science Subjects

Mathematics Mathematics and Statistics Computer Science Mathematics and Computer Science

All courses can be 3 or 4 years

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Maths in other subjects

For admissions, A-Level Maths is mentioned as a preparation for a number of courses:

Essential: Computer Science, Engineering Science, Engineering, Economics & Management (EEM), Materials, Economics & Management (MEM), Materials, Maths, Medicine, Physics

Desirable/Helpful: Biochemistry, Biology, Chemistry, Economics & Management, Experimental Psychology, History and Economics, Law, Philosophy , Politics & Economics (PPE), Physiological Sciences, Psychology, Philosophy & Physiology (PPP)

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Entrance Requirements

Essential: A-Level Mathematics Recommended: Further Maths or a

Science Note it is not a requirement to have

Further Maths for entry to Oxford For Computer Science, Further Maths is

perhaps more suitable than Computing or IT

Usual offer is AAA

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First Year Maths Course

Algebra (Group Theory) Linear Algebra (Vectors, Matrices) Calculus Analysis (Behaviour of functions) Applied Maths (Dynamics, Probability) Geometry

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Subsequent Years

The first year consists of compulsory courses which act as a foundation to build on

The second year starts off with more compulsory courses

The reminder of the course consists of a variety of options which become more specialised

In the fourth year, students have to study 6 courses from a choice of 40

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Mathematics and Statistics

The first year is the same as for the Mathematics course

In the second year, there are some compulsory units on probability and statistics

Options can be chosen from a wide range of Mathematics courses as well as specialised Statistics options

Requirement that around half the courses must be from Statistics options

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Computer Science

Computer Science Computer Science firmly based on Mathematics

Mathematics and Computer Science Closer to a half/half split between CS and Maths

Computer Science is part of the Mathematical Science faculty because it has a strong emphasis on theory

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Some of the first year CS courses

Functional Programming Design and Analysis of Algorithms Imperative Programming Digital Hardware Calculus Linear Algebra Logic and Proof Discrete Maths

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Subsequent Years

The second year is a combination of compulsory courses and options

Many courses have a practical component

Later years have a greater choice of courses

Third and Fourth year students have to complete a project

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Some Computer Science Options

Compilers Programming

Languages Computer Graphics Computer

Architecture Intelligent Systems Machine Learning Lambda Calculus Computer Security

Category Theory Computer Animation Linguistics Domain Theory Program Analysis Information Retrieval Bioinformatics Formal Verification

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Useful Sources of Information

Admissions: http://www.admissions.ox.ac.uk/

Mathematical Institute http://www.maths.ox.ac.uk/

Computing Laboratory: http://www.comlab.ox.ac.uk/

Colleges

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What is Computer Science?

It’s not just about learning new programming languages.

It is about understanding why programs work, and how to design them.

If you know how programs work then you can use a variety of languages.

It is the study of the Mathematics behind lots of different computing concepts.

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Simple Design Methodology

Try a simple version first Produce some test cases Prove it correct Consider efficiency (time taken and

space needed) Make improvements (called

refinements)

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Fibonacci Numbers

The first 10 Fibonacci numbers (from 1) are:

1,1,2,3,5,8,13,21,34,55

The Fibonacci numbers occurs in nature, for example: plant structures, population numbers.

Named after Leonardo of Pisa who was nicked named “Fibonacci”

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The rule for Fibonacci

The next number in the sequence is worked out by adding the previous two terms.

1,1,2,3,5,8,13,21,34,55

The next numbers are therefore34 + 55 = 8955 + 89 = 144

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Using algebra

To work out the nth Fibonacci number, which we’ll call fib(n), we have the rule:

fib(n) =

We also need base cases:fib(0) = 0 fib(1) = 1

This sequence is defined using previous terms of the sequence – it is an example of a recursive definition.

fib(n – 1) + fib(n – 2)

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Properties

The sequence has a relationship with the Golden Ratio

Fibonacci numbers have a variety of properties such as fib(5n) is always a multiple of 5 in fact, fib(a£b) is always a multiple of fib(a) and fib(b)

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Writing a computer program

Using a language called Haskell, we can write the following function:

> fib(0) = 0

> fib(1) = 1

> fib(n) = fib(n-1) + fib(n-2)

which looks very similar to our algebraic definition

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Working out an example

Suppose we want to find fib(5)

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Our program would do this…

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What’s happening?

The program blindly follows the definition of fib, not remembering any of the other values.So, for

(fib(3) + fib(2)) + fib(3)the calculation for fib(3) is worked out twice.

The number of steps needed to work out fib(n) is proportional to n – it takes exponential time.

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Refinements

Why this program is so inefficient is because at each step we have two occurrences of fib (termed recursive calls).

When working out the Fibonacci sequence, we should keep track of previous values of fib and make sure that we only have one occurrence of the function at each stage.

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Writing the new definition

We define > fibtwo(0) = (0,1)

> fibtwo(n) = (b,a+b)

> where (a,b) = fibtwo(n-1)

> newfib(n) = fst(fibtwo(n))

The function fst means take the first number

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Explanation

The function fibtwo actually works out:fibtwo(n) = (fib(n), fib(n +1))

We have used a technique called tupling – which allows us to keep extra results at each stage of a calculation.

This version is much more efficient that the previous one (it is linear time).

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An example of the new function

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Algorithm Design

When designing algorithms, we have to consider a number of things:

Our algorithm should be efficient – that is, where possible, it should not take too long or use too much memory.

We should look at ways of improving existing algorithms.

We may have to try a number of different approaches and techniques.

We should make sure that our algorithms are correct.

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Finding the Highest Common Factor

Example:Find the HCF of 308 and 1001.

1) Find the factors of both numbers: 308 – [1,2,4,7,11,14,22,28,44,77,154,308]1001 – [1,7,11,13,77,91,143,1001]

2) Find those in common [1,7,11,77]3) Find the highest

Answer = 77

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Creating an algorithm

For our example, we had three steps:1) Find the factors2) Find those factors in common3) Find the highest factor in common

These steps allow us to construct an algorithm.

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Creating a program

We are going to use a programming language called Haskell.

Haskell is used throughout the course at Oxford.

It is very powerful as it allows you write programs that look very similar to mathematical equations.

You can easily prove properties about Haskell programs.

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Step 1 We need produce a list of factors for a

number n – call this list factor(n). A simple way is to check whether each

number d between 1 and n is a factor of n. We do this by checking what the remainder

is when we divide n by d. If the remainder is 0 then d is a factor of n. We are done when d=n. We create factor lists for both numbers.

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Function for Step 1

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Step 2

Now that we have our factor lists, which we will call f1 and f2, we create a list of common factors.

We do this by looking at all the numbers in f1 to see if they are in f2.

We there are no more numbers in f1 then we are done.

Call this function: common(f1,f2).

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Function for Step 2

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Step 3

Now that we have a list of common factors we now check which number in our list is the biggest.

We do this by going through the list remembering which is the biggest number that we have seen so far.

Call this function: highest(list).

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Function for Step 3

If list is empty then return 0, otherwise we check whether the first member of list is higher than the rest of list.

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Putting the three steps together

To calculate the hcf for two numbers a and b, we just follow the three steps in order.So, in Haskell, we can define

Remember that when composing functions, we do the innermost operation first.

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Problems with this method

Although this method is fairly easy to explain, it is quite slow for large numbers.

It also wastes quite a lot of space calculating the factors of both numbers when we only need one of them.

Can we think of any ways to improve this method?

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Possible improvements

Remember factors occur in pairs so that we actually find two factors at the same time.

If we find the factors in pairs then we only need to check up to n.

We could combine common and highest to find the hcf more quickly (this kind of technique is called fusion).

Could use prime numbers.

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A Faster Algorithm

This algorithm was apparently first given by the famous mathematician Euclid around 300 BC.

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An example of this algorithm

hcf(308,1001)= hcf(308,693)= hcf(308,385)= hcf(308,77)= hcf(231,77)= hcf(154,77)= hcf(77,77)= 77

The algorithm works because any factor of a and b is also a factor of a – b

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Writing this algorithm in Haskell

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An even faster algorithm

hcf(1001,308) 1001 = 3 × 308 + 77= hcf(308,77) 308 = 4 × 77= hcf(77,0)= 77