Machine Learning 1
Code: 104870 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| 2503852 Applied Statistics | OB | 3 |
Contact
- Name:
- Antonio Lozano Bagen
- Email:
- antonio.lozano.bagen@uab.cat
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
This course assumes that the student has obtained the knowledge taught in different courses on the following topics:
- Calculus in several variables.
- Probability.
- Linear models.
- Python programming.
Objectives and Contextualisation
This course aims to familiarize the student with different methods of machine learning by applying the point of view used when large amounts of data are available.
Learning Outcomes
- CM11 (Competence) Create new machine learning models, running experiments to demonstrate their feasibility and improved performance compared to the state of the art.
- KM16 (Knowledge) Recognise supervised and unsupervised, profound and generic machine learning models, fostering innovation in the field of statistics.
- KM16 (Knowledge) Recognise supervised and unsupervised, profound and generic machine learning models, fostering innovation in the field of statistics.
Content
- Introduction to machine learning.
- Regularized linear and logistic regression.
- Statistical learning.
- Support vector machines.
- K-nearest neighbors.
- Naive Bayes.
- Decision trees.
- Ensembles.
- Text mining.
- Graph analysis.
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, KM16 |
| Practical Project | 50% | 20 | 0.8 | CM11, 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 grading for the practice part will have be based on deliverables during the course.
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 NT = min(5, NER), where NER is 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 a practical project (50%).
Bibliography
- Geron, A. (2019) Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (O'Reilly)
- Hastie, T. et al (2008) The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Software
Python
Language list
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 1 | Catalan | first semester | afternoon |
| (TE) Theory | 1 | Catalan | first semester | afternoon |