**Session 1**: **Introduction to Supervised Learning** —- *Ricardo Silva*

*Ricardo Silva*

**What Machine Learning Is About**

• Key General Concepts of Predictive Modelling

• Kernel Methods

• Tree-based Methods and Boosting

• Deep Learning

**Session 2**: **Introduction to Unsupervised learning** —- *Ricardo Silva*

*Ricardo Silva*

##### • Clustering

• Dimensionality Reduction

• Autoencoders and Sequence Learning

**Session 3**: **Introduction to Python** —- *Alberto Manfreda*

*Alberto Manfreda*

##### • Python basics: variables, expressions, indentation and comments

• Control flow and mathematical/logical operators

• Strings and basic string formatting

• Data structures (lists, tuples, dictionaries)

• Structured programming in Python

**Session 4**: **Python for Data Science and Machine Learning** —- *Alberto Manfreda*

*Alberto Manfreda*

##### • NumPy

• Pandas

• Matplotlib

• Scikit-learn – part I

**Session 5**: **Fundamentals of Causal Inference** —- *Ricardo Silva*

*Ricardo Silva*

##### • What Causal Inference is About

• Languages for Expressing Causal Assumptions

• The Identification Problem, a Structural Causal Model Perspective

• Machine Learning for Estimating Causal Effects

**Session 6**: **Advanced Methods in Machine Learning** —- *Ricardo Silva*

*Ricardo Silva*

##### • Further Developments in Deep Learning

• Principles of Graphical Models

• Basic Methods in Bayesian Nonparametrics

• An Overview of Reinforcement Learning

**Session 7**: **Further Python Programming Techniques and Tools for Data Science**

*Alberto Manfreda*

*Alberto Manfreda*

##### • Scikit-learn – part II

• Introduction to classes and objects

• PyTorch

**Session 8**: **Data Science for Cancer Research I** —- *Maria De Iorio*

*Maria De Iorio*

##### • Introduction to cell biology and measurement technologies

**Session 9**: **Data Science for Cancer Research II** —- *Maria De Iorio*

*Maria De Iorio*

##### • Statistical Inference for Genomic Studies

• Practical Session with Applications in Cancer Genomics

**Session 10**: **Data Science for Cancer Research II**I —- *Maria De Iorio*

*Maria De Iorio*

##### • Clustering and Data Integration

• Practical Session with Applications in Cancer Genomics

**References**:

**• Sessions 1,2,5,6**

a) G. James, D. Witten, T. Hastie and R. Tibshirani, “An Introduction to Statistical Learning”, b:

b) https://www.statlearning.com

c) Zhang et al., “Dive into Deep Learning”, https://d2l.ai J. Pearl, M. Glymour and N. Jewell, “Causal Inference in Statistics, a Primer”. http://bayes.cs.ucla.edu/PRIMER/

**•** **Sessions 3,4,7**

https://docs.python.org/3/tutorial

https://pytorch.org/tutorials/

https://jakevdp.github.io/PythonDataScienceHandbook