Teaching plan for the course unit

 

Close imatge de maquetació

 

Print

 

General information

 

Course unit name: Automatic Learning

Course unit code: 572664

Academic year: 2017-2018

Coordinator: Oriol Pujol Vila

Department: Department of Mathematics and Computer Science

Credits: 6

Single program: S

 

 

Estimated learning time

Total number of hours 150

 

Face-to-face learning activities

60

 

-  Lecture with practical component

 

60

Supervised project

45

Independent learning

45

 

 

Recommendations

 

Bring a laptop to the class.

 

 

Competences to be gained during study

 

  • To know how to hypothesize and develop the intuition about a data set using exploratory analysis techniques.

  • To efficiently and effectively apply machine learning analytic and predictive.

  • To understand, create, and modify analytic and exploratory algorithms operating over data.

  • To verify and quantify the validity of an hypothesis using data analytics.

 

 

 

 

Learning objectives

 

Referring to knowledge

The goal of this course is to understand the fundamental principles of machine learning. This includes, understanding supervised and unsupervised learning, general learning theory as well as good practices for properly use machine learning tools.

 

 

Teaching blocks

 

1. Understanding data

2. Learning theory

3. Supervised learning

3.1. Generative models

3.2. Discriminative models

4. Unsupervised learning

 

 

Teaching methods and general organization

 

Classes mix theory and practice. Concepts are given and then applied to different cases. It is advisable to bring a laptop to the class for following the course. Brief snippets of code are coded live.

 

 

Official assessment of learning outcomes

 

The course grade is the average of three grades, a final exam (T), deliverables (P), and quizzes (Q) done in class.

Final grade = (T+P+Q) /3 

Subject to:

(a) All parts must be graded in order for the former equation to be applied.

(b) The deliverables grade, P, must be above 4.

In the case all the constraints are not fulfilled the student does not pass the course.

All students that fulfil the constraints but after the final grade is computed they still do not pass the course have a one time opportunity of re-evaluation. The re-evaluation will just involve the marks of part T.

 

 

Examination-based assessment

In order to take this option the student must explicitly ask for it in secretary in due time.

In the case a student formaly ask for unique evaluation, final grade will be computed as follows:

Final grade = (T+P) /2

subject to

(a) All parts must be graded in order for the former equation to be applied.

(b) The deliverables grade, P, must be above 4.

In the case all the constraints are not fulfilled the student does not pass the course.

All students that fulfil the constraints but after the final grade is computed they still do not pass the course have a one time opportunity of re-evaluation. The re-evaluation will just involve the marks of part T.