Course Syllabus
Advanced Data Analytics • HEC Lausanne • Fall 2025
📍 Meeting Time & Location
Time: Mondays, 10:15 - 12:00 (Lecture) • 16:30 - 18:00 (Practice)
Location: Internef 126 (Morning) • Anthropole 3185 (Afternoon)
Course Overview
Learning Objectives
Gain practical familiarity with current computer-aided data analysis and machine learning approaches.
Course Structure
14-week Master's level course: Three 45-minute lectures + 45-minute hands-on session each Monday.
TA Sessions
Mondays 17:15-18:00 with Maria Pia Lombardo. Fridays by request.
Skills You'll Master
Supervised Learning
Regression • Classification • Deep Neural Networks
Unsupervised Learning
Clustering • PCA • Gaussian Mixture Models
Reinforcement Learning
Q-Learning • Portfolio optimization • If time permits
Deep Learning
TensorFlow • PyTorch • RNNs • LSTMs
Applied ML
Stock prediction • NLP • Real-world applications
Course Schedule
Detailed weekly materials are available in the Weekly Materials Hub.
Foundations
Weeks 1-5Core ML
Weeks 6-10Advanced Topics
Weeks 11-14Assessment & Grading
Capstone Project
Individual data science project • 10-page report • GitHub repository • Video presentation (max 10 min)
View Project Guidelines →No Exams
Assessment based entirely on demonstrating understanding through the capstone project
Exercise Sheets
8 problem sets distributed throughout the semester for practice and learning
Course References & Textbooks
An Introduction to Statistical Learning
James, Witten, Hastie, Tibshirani • Springer
Pattern Recognition and Machine Learning
Christopher Bishop • Springer