Course syllabus - Applied Machine Learning

Objectives

The goal of the course is to provide knowledge on machine learning techniques with a focus on its application that includes data, feature engineering, machine learning and deep learning models, and model evaluation.

Learning outcomes

After completing the course, the student should be able to: 

1. describe the fundamental needs, challenges and limitations of data and feature engineering, 
2. demonstrate the ability to analyse trade-off between different machine learning models  and
3. describe and understand the basic techniques of ML model evaluation and improvement.

Course content

Module 1: Learning from Data. 
Module 2:  Representing Data and Feature.  
Module 3: Supervised Machine Learning: Naive Bayes Classifiers, Ensembles of Decision Trees (RF) and Support Vector Machines.  
Module 4: Neural networks and Deep learning.  
Module 5: Unsupervised Machine Learning: PCA, t-SEN, Agglomerative Clustering, DBSCAN.
Module 6: Model Evaluation, Improvement, and ethical aspects.

Specific requirements

Discrete Mathematics 7.5 hp, Object Oriented Programming 7.5 hp, Machine Learning Concepts 7.5 hp, Basic Vector Algebra 7.5 hp or equivalent.

Examination

Written assignment (INL1), Comparison of the performance of different supervised models, 1,5 credits, examines the learning outcome 2, marks Fail (U) or Pass (G). 
Written assignment (INL2), Problem solving with unsupervised learning, 1 credit, examines the learning outcome 2, marks Fail (U) or Pass (G). 
Written assignment (INL3), Neural networks and Deep learning, 2 credits, examines the learning outcome 2, marks Fail (U) or Pass (G). 
Project (PRO1),  Project report, 3 credits, examines the learning outcome 1-3, marks Fail (U), 3, 4 or 5.

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Grading scale: 5, 4, 3

Interim Regulations and Other Regulations

The course overlaps 1.5 credits with DVA76 Deep Learning for Industrial Imaging and DVA453 Machine Learning With Big Data.