Module Details

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.
Title Machine Learning in Practice
Code CSCK503
Coordinator Prof FP Coenen
Computer Science
Coenen@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2020-21 Level 7 FHEQ Whole Session 15

Aims

1. To provide an in-depth understanding of established techniques of machine learning, its real-world application and the legal contexts in which machine learning operates.

2. To provide students with comprehensive knowledge of the nature of data and the mechanism that may be used to pre-process data to support machine learning activities.

3. To establish a comprehensive and practical awareness of the techniques and metrics used to evaluate machine learning algorithms.

4. To furnish students with a in-depth and critical knowledge of a range of established approaches to machine learning, including their statistical and mathematical underpinning.

5. To provide a wide-ranging practical knowledge of an established machine learning workbench.


Learning Outcomes

(M1) A well-founded and comprehensive knowledge of the operation of a widely used machine learning workbench.

(M2) A comprehensive and systematic understanding of the legal frameworks in which machine learning operates.

(M3) A practical ability to deploy effectively a variety of tools and techniques within the remit of machine learning.

(M4) A deep and systematic understanding of the limitations of a range of machine learning techniques and how the effectiveness of individual techniques can be analysed.

(S1) Communication skills in electronic as well as written form.

(S2) Self-direction and originality in tackling and solving problems.

(S3) An ability to act autonomously and professionally when planning and implementing solutions to computer science problems.

(S4) Experience of working in development teams, respecting others, co-operating, negotiating/persuading, awareness of interdependence with others.


Syllabus

 

1. Machine learning fundamentals
The idea of machine learning and the machine learning landscape, the nature of data, supervised and unsupervised learning, performance measures, bias and variance, data confidentiality the ethics of machine learning.

2. Data Preprocessing
The importance of data preparation, missing data, feature selection extraction, data standardization.

3. Dimensionality reduction
Principle Component Analysis (PCA). Visualising high-dimensional data.

4. Linear regression
Linear regression models. Evaluating linear aggression models. Multiple and Polynomial regression. Regularisation.

5. Classification
Binary classification. Classification performance metrics. Significance testing. Multi class classification and Multi label classification.

6. Decision Trees
Information gain, entropy. Ensemble learning. Random forests.

7. Association Rule Mining
Pattern mining. Suport and Confidence. Association rule min ing. Sequence mining. Rule induction.

8. Clustering
Cluster algorithms. Hieratchical clustering. Cluster configuration evaluation metrics. Clustering to learn features.


Teaching and Learning Strategies

The mode of delivery is by online learning, facilitated by a Virtual Learning Environment (VLE). This mode of study enables students to pursue modules via home study while continuing in employment. Module delivery involves the establishment of a virtual classroom in which a relatively small group of students (usually 10-25) work under the direction of a faculty member. Module delivery proceeds via a series of eight one-week online sessions, each of which comprises an online lecture, supported by other eLearning activities, posted electronically to a public folder in the virtual classroom. The eLearning activities will include lecture casts, live seminar sessions, self-assessment activities, reading materials and other multimedia resources. Communication within the virtual classroom is asynchronous, preserving the requirement that students are able to pursue the course in their own time, within the weekly time-frame of each seminar. An important element of the module provision is act ive learning through collaborative, cohort-based, learning using discussion fora where the students engage in assessed discussions facilitated by the faculty member responsible for the module. This in turn encourages both confidence and global citizenship (given the international nature of the online student body).


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

        40

64
Timetable (if known)              
Private Study 86
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Group Presentation: Machine learning group project resulting in a demonstrable system and group video report (10 minutes) describing and analysing the approach and the results obtained.  12 hours    30       
Programming: Individual machine learning challenge resulting in a demonstrable system and supporting analysis in the form of a brief report (500 words).  12 hours    30       
Discussion Question 2: Participate actively in an online discussion concerning one of the applications of machine learning covered within the module, demonstrating an understanding of the key issues a  1000-1500 words    20       
Discussion Question 1: Participate actively in an online discussion concerning the background to machine learning, demonstrating an understanding of the key issues and showing original thought.  1000-1500 words    20       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.