Module Specification
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Maths and Statistics for AI and Data Science
2. Module Code COMP533
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 7 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Professor LA Gasieniec Computer Science L.A.Gasieniec@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 36

   10

       46
17.

Private Study

 104
18.

TOTAL HOURS

 150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

 
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

COMP516 Research Methods in Computer Science; COMP517 Programming Fundamentals
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:
25. Programme(s) (including Year of Study) to which this module is available on a required basis:
26. Programme(s) (including Year of Study) to which this module is available on an optional basis:
27. Aims
 

This module aims to cover the key concepts and techniques from linear algebra,
differential calculus, probability theory and statistics. The acquired knowledge
will help the student to interpret the results generated during data analysis.
 
28. Learning Outcomes
 

(LO1) Good understanding of basic mathematical principles and methods of interest to data scientists. The main focus is on differential calculus and linear algebra.
 

(LO2) Critical awareness of basic and more specialised concepts in probability theory and statistics relevant to data science.
 

(LO3) Ability to undertake a small software project in the domain of data science.
 

(LO4) Ability to communicate the outcome of experimental work in the domain of data science.
 

(S1) Problem Solving – Numeracy and computational skills.
 

(S2) Problem Solving – Analysing facts and situations and applying creative thinking to develop appropriate solutions.
 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Not yet decided

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: On-campus synchronous sessions
 
30. Syllabus
   

DIFFERENTIAL CALCULUS (3 weeks)
- Review of basic calculus: numbers, sets, functions, limits,
- basic geometry: coordinates, lines, trigonometry
- Differential calculus: limits, continuity, derivatives, velocity, concavity
- Optimisation: minima/maxima, gradient descent, second order methods (Newton)

LINEAR ALGEBRA (3 weeks)
- Basic concepts: vectors, matrices, dot products, matrix product
- Geometry of matrices and derivatives, linear transformations and partial derivatives
- Extensions: eigen values and vectors, determinants
linear basis and projections, eigen-decomposition & SVD, pseudoinverse.

PROBABILITY THEORY (3 weeks)
- Basic probability: events, sample space, frequentist vs Bayesian approach,
law of large numbers, conditional probability, independence, Bayes theorem,
random variables
- Probability distributions, probability sampling, random sa mpling, sampling distributions

STATISTICS (3 weeks)
Measures of Centre and Variation,
Statistical Significance (Confidence intervals) and Tests of Hypothesis
- errors, chi-square independence test,
Correlation vs causation, Linear Regression
Descriptive Statistics: Data and Data Presentation: scatter plots, line graphs, bar charts, histograms, box plots
 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment
32. EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  (533) Final Exam. There is a resit opportunity. This is an anonymous assessment. 2.5 60
33. CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  (533.4) (Video) presentation. There is a resit opportunity [a part of the resit exam]. 10 10
  (553.3) Programming assignment There is a resit opportunity [a part of the resit exam] 10 10
  (533.2) Theory assignment 2. There is a resit opportunity [a part of the resit exam]. 10 10
  (533.1) Theory assignment 1. There is a resit opportunity [a part of the resit exam]. 5 10