Lecture
This lecture introduces the fundamentals of advanced machine learning, covering topics such as dimensionality reduction, clustering, classification, regression, and probabilistic methods. The course emphasizes practical applications through mini-projects, coding exercises, and in-class paper readings. Students are expected to be familiar with various machine learning methods and evaluation techniques. The class format includes interactive lectures, exercises, and practice sessions. Grading is based on personal work, mini-projects, and a final oral exam. The course explores advanced topics like Kernel PCA, K-means clustering, probabilistic regression, and reinforcement learning. Applications of probabilistic regression techniques in object exploration and shape reconstruction are also discussed.