Operations Research
Code: 100125Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Mathematics | OP | 4 |
Contact lecturer
- Name :
- Rosario Delgado De la Torre
- Email :
- rosario.delgado@uab.cat
Teaching staff
- Anabel Blasco Moreno
- David Moriña Soler
Group languages
You can consult this information at the end of the document.
Prerequisites
This course assumes that the student has acquired the knowledge taught in various subjects on the following topics:
- Linear Algebra and Calculus
- Probability and Statistical Inference
- Computer Tools for Statistics and Introduction to Programming
- Linear Models
Objectives
This course aims to introduce students to the field of Supervised Machine Learning by presenting various methodologies and basic concepts.
Learning outcomes
- Effectively use bibliographies and electronic resources to obtain information.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
- Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
- Achieve mastery and security in the handling of specific scientific programs for problem-solving with real data and in order to perform simulations.
- Dominate the basic concepts of the theory and be able to combine them and use them to resolve problems.
- Understand the rudiments of logistics and other fields in which operative research is applied to the technological and industrial fields
- Find models of scientific or topological reality in relation to a decision-making problem and express it using the mathematical language of optimisation problems with dynamic programming or stochastic queues.
- Draw adequate conclusions from the result of the model.
- Distinguish, of a problem, which thing is important of expensive to the building of the mathematical model and his resolution of what is not it.
- Evaluate the difficulty to do a calculation of analytical probabilities in complex situations and know distinguish when can realise these calculations and when has to resort to the simulation stochastic.
- Know generate and manipulate models of simulation of the reality to establish and check hypothesis in the study of problems or realities more complex.
- Actively demonstrate high concern for quality when defending or presenting the conclusions of one's work.
Contents
- Introduction to Supervised Machine Learning.
- Reinforcement Learning
- Support Vector Machines
- K-Nearest Neighbors
- Decision Trees and Random Forests
- Validation, confusion matrices, and performance metrics (binary case)
- Bias and fairness in Machine Learning
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Theory sessions | 50 | 2 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Personal study of the subject | 46 | 1.84 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Lab sessions | 30 | 1.2 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
Teaching will combine face-to-face lectures with hands-on problem-solving and computer-based practical activities.
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.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Resit examen | 75% | 3 | 0.12 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Computed-based practical exam (classroom) | 15% | 3 | 0.12 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| PAC1 (problem solving test) | 25% | 3 | 0.12 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Exam | 50% | 3 | 0.12 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| PAC2. Deliveries of problems and exercises with computer (classroom) | 10% | 12 | 0.48 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
Continuous Assessment
The assessment of the course consists of four components: the grade obtained in PAC1 (a mid-semester problem-solving test), the grades obtained from assignments submitted during problem-solving sessions and/or computer-based practical activities (PAC2), the grade obtained in the computer-based practical exam held during the last practical session (Eprac), and the grade obtained in the final problem-solving examination (Ex).
The course grade will be calculated as
N = 0.25*PAC1 + 0.10*PAC2 + 0.15*Eprac + 0.50*Ex,
provided that each component is at least 3.5 out of 10. Otherwise, any component with a grade below 3.5 will be counted as 0 in the calculation of N.
If N ≥ 5, the course will be considered passed and the final grade (FG) will be FG = N.
Otherwise, students may take a resit examination (ExRec). In this case, the final grade will be calculated as
FG = 0.75*ExRec + 0.10*PAC2 + 0.15*Eprac.
Therefore, neither the PAC2 assignments nor the practical examination are recoverable.
Under no circumstances may the resit examination be used to improve the grade of a course that has already been passed.
A student will be considered assessable if they have participated in at least one assessment activity. Otherwise, the final record will state “Not Assessed”.
Single Assessment
For students who opt for the single-assessment modality, the final grade will be based on the final examination (75%) and a computer-based practical examination (25%).
USE OF ARTIFICIAL INTELLIGENCE
For this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks, such as literature and information searches, text revision, or translation.
Students must clearly identify, where applicable, which parts of their submitted work have been generated with the assistance of AI technologies, specify the tools used, and include a critical reflection on how these tools have influenced both the process and the final outcome of the activity.
Failure to disclose the use of AI in an assessment activity will be considered a breach of academic integrity and may result in a partial or total reduction of the grade for that activity, or in more severe disciplinary measures in serious cases.
Any irregularity committed in an assessment activity (academic fraud, plagiarism, or improper use of Artificial Intelligence, unless such use is expressly authorized in the course syllabus) that may lead to a significant alteration of the grade will result in that assessment activity being graded with a 0 (fail).
If the course syllabus establishes that obtaining a minimum grade in that assessment activity is an essential requirement to pass the course, or if multiple irregularities are detected in the assessment activities of the same course, the final grade for the course will be 0 (fail).
In addition, disciplinary proceedings may be initiated against any student who commits any of these irregularities.
Bibliography
Basic bibliography
James, G., Witten, D., Hastie, T., & Tibshirani, R. An Introduction to Statistical Learning: with Applications in R (and An Introduction to Statistical Learning: with Applications in Python). Copies are available through the university library, both in print and online.
Additional bibliography
- Géron, A. (2019, 2nd ed.). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
- Hastie, T., Tibshirani, R., & Friedman, J. (2016, 2nd ed.). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Information Science and Statistics Series. Springer.
- Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Series in Computer Science: Artificial Intelligence.
- Sutton, R. S., & Barto, A. G. (2018, 2nd ed.). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
- Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. Available at: fairmlbook.org
Software
Python and RStudio will be used, the latter being an IDE (Integrated Development Environment) specifically designed for the R programming language.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE