Explores postmortem memory analysis using news and social media data, investigating biographic correlates and memory patterns.
Explores cache memory design, hits, misses, and eviction policies in computer systems, emphasizing spatial and temporal locality.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores memory cache principles, emphasizing spatial locality, latency impact, and cache efficiency strategies.
Explores making memories, forgetting, and the Forgetting Curve in memory retention and learning.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
