XVII Madrid UPM Machine Learning and Advanced Statistics Summer School (June 16th - June 27th, 2025)

Dear colleagues,

The Technical University of Madrid (UPM) will once more organize the 'Madri=
d UPM Machine Learning and Advanced Statistics' summer school. The summer s=
chool will be held in Boadilla del Monte, near Madrid, from June 16th to Ju=
ne 27th. This year's edition comprises 12 week-long courses (15 lecture hou=
rs each), given during two weeks (six courses each week). Attendees may reg=
ister in each course independently. No restrictions, besides those imposed =
by timetables, apply on the number or choice of courses.

Early registration is now *OPEN*. Extended information on course programmes=
, price, venue, accommodation and transport is available at the school's we=
bsite:

https://www.dia.fi.upm.es/MLAS

There is a 25% discount for members of Spanish AEPIA and SEIO societies. =
=20

Please, forward this information to your colleagues, students, and whomever=
 you think may find it interesting.

Best regards,

Pedro Larra=C3=B1aga, Concha Bielza, Bojan Mihaljevi=C4=87 and Laura Gonzal=
ez Veiga.
-- School coordinators.

*** List of courses and brief description ***

# Week 1 (June 16th - June 20th, 2025)=20

## 1st session: 9:45-12:45

### Course 1: Bayesian Networks (15 h)
      Basics of Bayesian networks. Inference in Bayesian networks. Learning=
 Bayesian networks from data. Real applications. Practical demonstration: R=
.

### Course 2: Time Series(15 h)
      Basic concepts in time series. Linear models for time series. Time se=
ries clustering. Practical demonstration: R.
     =20
## 2nd session: 13:45-16:45

### Course 3: Supervised Classification (15 h)
      Introduction. Assessing the performance of supervised classification =
algorithms. Preprocessing. Classification techniques. Combining multiple cl=
assifiers. Comparing supervised classification algorithms. Practical demons=
tration: python.=20

### Course 4: Reinforcement learning (15 h)
      Introduction. Dynamic programming methods. Temporal-difference learni=
ng. Policy gradient methods. Causal reinforcement learning. Practical demon=
stration: R. =20

## 3rd session: 17:00 - 20:00

### Course 5: Deep Learning (15 h)
      Introduction. Learning algorithms. Learning in deep networks. Deep Le=
arning for Computer Vision. Deep Learning for Language. Practical session: =
Python notebooks with Google Colab with keras, Pytorch and Hugging Face Tra=
nsformers.

### Course 6: Bayesian Inference (15 h)
      Introduction: Bayesian basics. Conjugate models. MCMC and other simul=
ation methods. Regression and Hierarchical models. Model selection. Practic=
al demonstration: R and WinBugs.
     =20

# Week 2 (June 23rd - June 27th, 2025)

## 1st session: 9:45-12:45=20

### Course 7: Causality (15 h)
    Introduction. Causal graphs. Mediation analysis. Sensitivity analysis t=
o unmeasured confounding. Counterfactual reasoning. Practical sessions: R.

### Course 8: Clustering (15 h)
      Introduction to clustering. Data exploration and preparation. Prototy=
pe-based clustering. Density-based clustering. Graph-based clustering. Clus=
ter evaluation. Miscellanea. Conclusions and final advice. Practical sessio=
n: R.

## 2nd session: 13:45-16:45

### Course 9: Gaussian Processes and Bayesian Optimization (15 h)
      Introduction to Gaussian processes. Sparse Gaussian processes. Deep G=
aussian processes. Introduction to Bayesian optimization. Bayesian optimiza=
tion in complex scenarios. Practical demonstration: python using GPytorch a=
nd BOTorch.
     =20
### Course 10: Explainable Machine Learning (15 h)
      Introduction. Inherently interpretable models. Post-hoc interpretatio=
n of black box models. Basics of causal inference. Beyond tabular and i.i.d=
. data. Other topics. Practical demonstration: Python with Google Colab.=20
        =20
## 3rd session: 17:00-20:00

### Course 11:  Generative AI (15 h)
     Introduction to the course. Neural networks and deep learning. Generat=
ive AI for images. Generative AI for language. Hands-on session: Pytorch, V=
AEs, GANs, diffusion models, LLMs, aligning a generative LLM, using an open=
-source image generation model.
     =20
### Course 12: Feature Subset Selection (15 h)
      Introduction. Filter approaches. Embedded methods. Wrapper methods. A=
dditional topics. Hands-on sessions: R and python.

########################################################################

To unsubscribe from the IR list, click the following link:
https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=3DIR&A=3D1

This message was issued to members of www.jiscmail.ac.uk/IR, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/