Semestr Letni 2019-2020

The Selective Course for Computer Science

Learning is an internal mental process that integrates new information into established mental frameworks and updates those frameworks over time. This course provides a broad introduction to machine learning and some applications. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); learning theory (bias/variance tradeoffs, VC dimension); reinforcement learning. The course will also discuss recent applications of machine learning.

Lecture plan

  1. Lecture 1: Introduction to machine learning, Naive Bayes Classifier
  2. Lecture 2: Some famost classifiers: k-NN and Decision Tree
  3. Lecture 3: Methods for Classifier Evaluation
  4. Lecture 4: Support Vector Machine. The old slides are under this link
  5. Lecture 5: Regressions (I): maximum likelihood estimation, linear regression, least squares ridge regression, bias-variance
  6. Lecture 6: Regressions (II): logistic regression, Support Vector Regression, kernelized methods, Gaussian processes. A summary of supervised learning methods
  7. Lecture 7: Neural Networks and Deep learning: introduction
  8. Lecture 8: Deep learning: some special networks CNN, DBN and GAN
  9. Lecture 9: Unsupervised Learning: Clustering and Gaussian Mixture Model. The old slides are under this link
  10. Lecture 10: Unsupervised Learning: PCA, SVD, Matrix factorization and Latent factor model
  11. Lecture 11: Reinforcement Learning: Introduction. And also here and here
  12. Lecture 12: Reinforcement Learning: Some Approaches
  13. Lecture 13: Computational Learning theory
  14. Lecture 14: Boosting techniques