10 x 2 hours + 6 x 3 hrs Labs = 38 hours (6 weeks: 11/01-19/02)
1. Introduction: choosing the features and the metric.
2. Lab 1
3. Dimensional reduction and manifold learning
i. Linear methods: principal component analysis and multidimensional scaling
ii. Curved manifolds: ISOMAP, kernel PCA and Sketchmap
iii. Lab 2
iv. Diffusion Map and Stochastic Neighbor Embedding
v. Characterizing the embedding manifold: the intrinsic dimension
vi. Lab 3
4. Estimating the probability density
i. Parametric density estimators
ii. Non-parametric estimators: Histograms, Kernel density estimator and k-nearest neighbor estimator
iii. Adaptive density estimators
iv. Lab 4
5. Clustering
i. Partitioning schemes: k-means, k-medoids and k-centers.
ii. hierarchical and spectral clustering
iii. Lab 5
iv. Density-based clustering
v. Clustering techniques exploiting kinetic information
vi. Lab 6
Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.