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Weekly Materials

Lectures, practice sessions, and assignments for each week

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Week 1: Introduction to ML

Machine Learning Basics • Nuvolos Setup

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Part I: Foundations & Python

1 Sep 15

Introduction to ML

Machine Learning • Nuvolos Setup

2 Sep 22

No Class - Holiday

Swiss Federal Fast

3 Sep 29

Python Crash Course

Python • Jupyter • NumPy • SciPy • GitHub

4 Oct 6

Supervised Learning - Regression

Linear/Polynomial Regression • Gradient Descent

5 Oct 13

Generative AI & LLMs

Programming with AI • Autonomous Agents

6 Oct 20

Supervised Learning - Classification

k-NN • Naive Bayes • Decision Trees • Boosting

Part II: Deep Learning & Neural Networks

7 Oct 27

Deep Learning Basics

MLPs • Feed-forward • SGD • Backpropagation

8 Nov 3

Advanced Deep Learning

TensorFlow • PyTorch • RNNs • LSTMs

9 Nov 10

Recap Week

Review All Concepts • Q&A • NLP Applications

10 Nov 17

Gaussian Processes

GP Regression • Kernels • Option Pricing

Part III: Advanced Topics

11 Nov 24

Dimensionality & Active Learning

Curse of Dimensionality • PCA • Active Subspaces

12 Dec 1

Unsupervised Learning

k-Means • GMM • EM • Hierarchical/DBSCAN

13 Dec 8

Reinforcement Learning

RL Intro • Q-Learning • Portfolio Optimization

14 Dec 15

Applications & Wrap-up

Deep Uncertainty Quantification • Projects

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Advanced Data Analytics • HEC Lausanne • 2025

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