Statistical machine learning
Statistical machine learning
Academic year 2020/2021
- Course ID
- Silvia Montagna
- 2nd year
- Teaching period
- First semester
- D.M. 270 TAF C - Related or integrative
- Course disciplinary sector (SSD)
- SECS-S/01 - statistica
- Formal authority
- Type of examination
- MAT0035 Statistical Inference
MAT0041 Multivariate Statistical Analysis
Good knowledge of R is required
Sommario del corso
The course introduces methods and models to extract important patterns and trends from big amount of data, and presents basic concepts of machine learning and data mining from a statistical perspective. Topics covered include modern regression, classification, cross validation, model selection and regularisation, and tree-based methods, among others. The course emphasizes selection of appropriate methods and justification of choice, use of programming for implementation of the method, and evaluation and effective communication of results in data analysis reports.
Results of learning outcomes
Knowledge and understanding
- Advances knowledge of parametric and nonparametric models for prediction and classification
Applying knowledge and understanding
- Ability to convert various problems and data into statistical models to perform prediction/classification
- Students will be able to discern the different aspects of statistical learning in modern settings
- Students will properly use statistical language to comunicate the results of their findings
- The skills acquired will give students the opportunity to improve and deepen their knowledge of statistical modeling
Introduction to Statistical Learning
- Context and motivations
- Trade-off between goodness-of-fit and model complexity (i.e. variance and bias)
- Training and test set
- Exploratory data analysis
- Simple & multiple linear regression
- Residual analysis & model checking
Classification: Logistic regression; Multinomial logit/probit regression
Resampling methods: Cross-validation, bootstrap
- Subset selection
- Shrinkage methods
- Dimension reduction methods
- Polynomial regression
- Step functions
- Splines & smoothing splines
- Generalised additive models
- Regression & classification trees
- Bagging, boosting, random forests
Support vector machines
Introduction to neural networks
The course is composed of 48 hours of class lectures. Half of the lectures are devoted to the theorerical aspects of statistical machine learning, and the remaining half to their practical implemetation. We will use R as a programming language for data analysis and use existing packages written in R to support the course. Students will use RMarkdown for creating HTML and pdf documents.
Learning assessment methods
1) Winter (January/February) exam session: your final grade will be based on a weighted average of a data analysis project (60%) and a closed-books written exam on theory (40%). For the data analysis, students can work individually or in teams (max 3 people) on a project of their choosing. Each student/team is required to prepare a short presentation summarising the project to the class. Oral project presentations will be held during the last week of classes and will be evaluated, counting towards the final grade for the data analysis part. Further, each student will have to submit an individual project report due on Friday, December 20.
For students who have failed to submit their data analysis (in total or in part), case (2) below applies.
2) Summer and Fall exam sessions: the final exam consists of a long written test (4 hours) on theory (50%) and data analysis in R (50%).
Until the end of the Covid-19 emergency, the written exam will be held remotely via Webex with video surveillance. More specific instructions will be given to students registered to the exam via their institutional email addresses.
Suggested readings and bibliography
This is an applied course, which will be based on:
- JAMES, WITTEN, HASTIE, TIBSHIRANI. An introduction to statistical learning with applications in R. Springer.
This is available freely at www-bcf.usc.edu/~gareth/ISL. You are welcome to download it and print it out.
Another useful resource (also available freely online) is:
- HASTIE, TIBSHIRANI AND FRIEDMAN. The elements of statistical learning: data mining, inference and prediction. Springer-Verlag.
Slides for the course will be provided. If you see any typos in my notes (no matter how small), please tell me about them! Doing so will not only benefit you, but also myself, your classmates and any future students of this course.
This course will be delivered at the ESOMAS Department.
Electronic communication: I will occasionally send e-mails to the class (to the account listed for you in the SDS directory), so please check that account regularly.
- Enrollment opening date
- 01/09/2019 at 00:00
- Enrollment closing date
- 30/06/2020 at 00:00