Machine Learning I: Algorithms and Applications

 
Week   Exercise Lecture
  Mon. Content Thu. Content
1   (no exercise) 24.10. - organisational matters
- Machine Learning:
   Introduction and History (K/S)
  Homework: Perceptron learning rule
2 28.10. - homework review
- Tutorial (M):
   probability theory		 31.10. Bayes Rule (S):
- central role in statistics
- derivation and formula
- use for machine inference
  Homework: Ovarian cancer screening
3 04.11. - homework review
- Tutorial (M):
   differentiation		 07.11. ML Modeling (S):
- modeling tasks: regression, classi-
  fication, density estimation
- maximum likelihood (ML) loss fn.s
  Homework: See page 12 of lecture notes
Reading:  Maximum Likelihood - Mixture of Gaussians
4 11.11. - homework review
- lecture review		 14.11. Density Estimation (S):
- parametric vs. non-parametric
- classification via density estim.
- semi-parametric & mixture models
- Expectation-Maximisation (EM)
  Reading: pages 1-3 of  A Gentle Tutorial of the EM Algorithm
Reading: chapters 1-4 of Conjugate Gradient Without the Pain
5 18.11. - lecture review 21.11. Least-Squares Regression (S):
- linear vs. non-linear models
- simple gradient descent, SVD
- basis functions, generalized LS
- classification via regression
  Homework: questions
6 25.11. - homework review
- lecture review		 28.11. Overfitting & Validation (M):
- problem of overfitting
- empirical vs. true risk
- cross-validation
  Homework: questions
7 02.12. - homework review
- lecture review		 05.12. Penalization & Model Selection (M):
- Penalization
- Ockham's razor
- structural risk minimization
- minimum description length
  Homework: questions
8 09.12. lecture review 12.12. Neural Networks (M):
- biological background
- learning in neural networks
- backpropagation algorithm
  Reading: Lectures 1 and 2 of NN Course
9  16.12. review (M):
backpropagation		 19.12. Training Methods (M):
- learning rate adaptation
- quasi-Newton methods
- conjugate gradient
  Programming Assignment: Handout
10   (no exercise) 09.01. Classification (K):
- Fisher's linear discriminants
- k-nearest neighbor
- vector quantisation
11 13.01. lecture review 16.01. TBA
12 20.01. lecture review 23.01. Dimensionality Reduction (K):
- curse of dimensionality
- principal components analysis
- nonlinear autoencoding
13 27.01. lecture review 30.01. Self-Organising Maps (K)
14 03.02. lecture review 06.02. Summary lecture