Slides
- T0 Course Organization
- T1 Machine Learning, Univariate Data Analysis
- T2 Associative Learning: Decision Trees
- T3 Predictive Model Evaluation
- T4 Bayesian Learning
- T5 Lazy Learning
- T6 Regression
- T7 Evaluation: advanced aspects
- T8 Perceptron, Gradient Descent
- T9 Neural Processing, Deep Learning
- T10 Clustering
- T11 Dimensionality Reduction
- (uncovered) RBFs and SVMs
Updates or corrections
- T0 slide 13: updated on 14/9
- T5 slide 19: dummification can duplicate the Hamming distance (correction)
- T9 slide 13: updated sigmoid unit rule on 7/11
- T10 slides 30-31: updated on 20/10
- T10 slide 22: updated on 29/10
- Exam 2: 4 MLP #params = (1+m)10 + (10+1)10 + (10+1)