Module Details

Module Code: DATA C9004
Full Title: Machine Learning
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 10
Module Owner:: Rajesh Jaiswal
Departments: Unknown
Module Description: This module covers methods involved in designing and developing computer based programs that learn and improve with experience to make meaningful predictions based on test data. This module will focus on the concepts based on probability, statistics and optimization to train machine learning models.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Segregate and discuss a variety of machine learning algorithms
MLO2 Outline the critical features of supervised and un-supervised learning
MLO3 Research the types of problems that machine learning algorithms can solve
MLO4 Compare various methods of training and optimization of computer programs that is obtained through learning from data
MLO5 Design and train machine learning algorithms for independent and identically distributed data
MLO6 Establish the data analyst role in constructing the machine learning solutions.
MLO7 Evaluate and Analyse the performance of a selected of machine learning model and its solution.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
 
Module Indicative Content
Introduction
AI background, what is machine learning?, the five tribes
Categories of Machine Learning Algorithms
Supervised Learning- Classification and Regression, Unsupervised Learning - Clustering
Supervised Learning - Classification
Discriminant Analysis, Support Vector Machines, Naive Bayes, Random Forest, Nearest Neighbor
Supervised Learning - Regression
Linear Regression, GLM, Ensemble Methods, Decision trees, Neural Network - MLP, Back Propagation, RNN and CNN. Intro to deep learning
Unsupervised Learning - Clustering
K-means, Fuzzy C -means, Hierarchical - clustering basis functions, Gaussian Mixture, HMM, Neural Network - Self Organizing Maps (2D)
Module Assessment
Assessment Breakdown%
Course Work50.00%
Project50.00%
Module Special Regulation
 

Assessments

Full Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 2 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
CA1 - Assignment to identify and analyse the features of machine learning algorithms
Assessment Type Continuous Assessment % of Total Mark 40
Marks Out Of 100 Pass Mark 40
Timing n/a Learning Outcome 3,4,5,7
Duration in minutes 0
Assessment Description
CA2- Two assignments (20% each) to identify, design, and evaluate performance of the chosen machine learning algorithms to solve a given data analytics problem
Project
Assessment Type Group Project % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 3,4,5,6,7
Duration in minutes 0
Assessment Description
Group Project will consist of the following deliverable - Project proposal, Progress report and Project presentation. - Students will given a data related problem and will be asked to propose a solution based on machine learning model. Students will design and train and further analyse the performance of machine learning model and its solution
No Practical
No Final Examination

Part Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 10
Marks Out Of 100 Pass Mark 40
Timing Week 2 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
CA1- Assignment to identify and analyse the features of machine learning algorithms
Assessment Type Continuous Assessment % of Total Mark 40
Marks Out Of 100 Pass Mark 40
Timing n/a Learning Outcome 3,4,5,7
Duration in minutes 0
Assessment Description
CA2- Two assignments (20% each) to identify, design, and evaluate performance of the chosen machine learning algorithms to solve a given data analytics problem
Project
Assessment Type Group Project % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 3,4,5,6,7
Duration in minutes 0
Assessment Description
Group Project will consist of the following deliverable - Project proposal, Progress report and Project presentation. - Students will given a data related problem and will be asked to propose a solution based on machine learning model. Students will design and train and further analyse the performance of machine learning model and its solution
No Practical
No Final Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.
Reassessment Description
Individual Project - Students will given a data related problem and will be asked to identify and analyse the performance of machine learning model and its solution. This project will cover all the learning outcomes of the module.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact 1 hour lecture to cover the theory of machine learning Every Week 1.00 1
Practical Contact Two 2-hour lab per week to cover the tutorial and practicals of the module Every Week 4.00 4
Directed Reading Non Contact Lecture notes, books and web resources Every Week 2.00 2
Independent Study Non Contact Lecture notes, books and web resources Every Week 9.00 9
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 5.00
Workload: Part Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact 1 hour lecture to cover the theory of machine learning Every Week 1.00 1
Practical Contact Two 2-hour lab per week to cover the tutorial and practicals of the module Every Week 4.00 4
Directed Reading Non Contact Lecture notes, books and web resources Every Week 2.00 2
Independent Study Non Contact Lecture notes, books and web resources Every Week 9.00 9
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 5.00
 
Module Resources
Recommended Book Resources
  • Sarah Guido, Andreas Müller. (2016), Introduction to Machine Learning with Python, O'Reilly Media.
Supplementary Book Resources
  • Aurelien Geron. (2019), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media.
This module does not have any article/paper resources
Other Resources