This version of the course guide is provisional until the period for editing the new course guides ends.

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Machine Learning 2

Code: 104871 ECTS Credits: 6
2025/2026
Degree Type Year
Applied Statistics OB 3

Contact

Name:
Víctor Navas Portella
Email:
victor.navas@uab.cat

Teachers

Roger Borràs Amoraga

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

The first year subjects, in addition to Numerical Methods and Optimization and Machine Learning 1.

 


Objectives and Contextualisation

To learn at theoretical and practical levels the potential of deep learning for structured and also unstructured data.


Learning Outcomes

  1. CM11 (Competence) Create new machine learning models, running experiments to demonstrate their feasibility and improved performance compared to the state of the art.
  2. CM12 (Competence) Assess the existence of inequalities on the grounds of gender in databases, to avoid bias in automatic (algorithmic) decision-making.
  3. KM16 (Knowledge) Recognise supervised and unsupervised, profound and generic machine learning models, fostering innovation in the field of statistics.

Content

Topic 1: Introduction to Deep Learning Models
Topic 2: Neural Network-Based Learning
Topic 3: Learning Solutions


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Lab sessions 30 1.2
Type: Supervised      
Theory sessions 50 2
Type: Autonomous      
Personal study of the subject 46 1.84

Teaching will combine classroom lessons by teachers and practical work for students with a computer.

In all aspects of teaching/learning activities, the best efforts will be made by teachers and students to avoid language and situations that can be interpreted as sexist.

To achieve continuous improvement in this subject, everyone should collaborate in highlighting them.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Exam 50% 4 0.16 CM11, CM12, KM16
Practical Part 50% 20 0.8 CM11, CM12, KM16

Continuous grading

The grading for the course will be done in two parts: the theory part, NT, and the practice part, NP. The final grade for the course will be N = 0.5*NT + 0.5*NP.

The grading for the theory part will be based in two exams: a partial exam, NEP, and a final exam, NEF. The final grade for the theory part will be NT = max(NEF, 0.3*NEP + 0.7*NEF), as long as NEF is higher than 3,5, otherwise NT = NEF.

The evaluation of the practical part will consist of two items: a practical assignment (PA) and a practical exam (PE). The grade for the practical part will be PG = 0.5 PA + 0.5 PE.

On the day of the second-chance exam only the grade for the theory part will be updated. If a student goes to the second-chance exam then the theory grade, NT, will be the grade for the second-chance exam.

In order for an activity to be taken into account in the final grade, the activity grade has to be a minimum of 3,5. If NT or NP are below 3,5, then the final grade for the course will be N = min(NT, NP).

The student who has submitted works for at least 50% of the subject will be considered evaluable. Otherwise, it will appear in the record as non-evaluable.

 

Single grading

The grading for a student who chooses to be evaluated with the single grading modality will be based on the final examn grade (50%) and the grade for the practical assignement and the practical exam (50%).


Bibliography

  • Prince, S. (2023) Understanding Deep Learning
  • Geron, A. (2019) Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (O'Reilly)
  • Goodfellow, I. et al (2016) Deep Learning (MIT Press)
  • Chollet, F. (2017) Deep Learning with Python (Manning)

Software

Python


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.

Name Group Language Semester Turn
(PLAB) Practical laboratories 1 Catalan second semester afternoon
(TE) Theory 1 Catalan second semester afternoon