/EDAN95-Applied-Machine-Learning

This is the repository for the EDAN95 - Tillämpad maskininlärning (Applied Machine Learning) course given at Lunds Tekniska Högskola (LTH) during the Fall 2019 term.

Primary LanguageJupyter Notebook

EDAN95 - Applied Machine Learning

Lund University (LTH) | HT2 2019

This is the repository for the EDAN95 - Tillämpad maskininlärning (Applied Machine Learning) course given at Lunds Tekniska Högskola (LTH) during the Fall 2019 term.

Contents

The following topics are covered in the lab assignments:

  • Decision trees
  • ID3 algorithm
  • Multi-class image classification
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Long-Short Term Memory (LSTM)
  • Named Entity Recognition (NER)
  • N-grams
  • Language models
  • Word embeddings
  • Naive Bayes Classifier (NBC)
  • Nearest Centroid Classifier (NCC)
  • Gaussian Naive Bayes Classifier (GNBC)
  • Gaussian Mixture Models (GMM)
  • Expectation-Maximization algorithm (EM)
  • k-Means clustering
  • k-Nearest Neighbors (KNN)
  • Reinforcement Learning (RL)
  • Markov Decision Process (MDP)
  • Monte Carlo Methods for On-policy Prediction and Control

Other topics covered in the course lectures and reading material:

  • Python and linear algebra fundamentals
  • Probability and information theory
  • Machine learning fundamentals (linear and logistic regression, perceptron)
  • Machine learning concepts (loss, regularisation, evaluation, overfitting)
  • Neural network fundamentals (feed forward networks, backpropagation)

Material

Course literature:

  • Kevin P. Murphy: Machine Learning, A Probabilistic Perspective. MIT Press, 2012, ISBN: 9780262018029.
  • Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press, 2016, ISBN: 9780262035613.
  • François Chollet: Deep Learning with Python. Manning, 2018, ISBN: 9781617294433.

Other related literature:

  • Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2017, ISBN: 9781491962299.
  • Tom Mitchell: Machine Learning. McGraw Hill, 1997, ISBN: 0070428077.
  • David L. Poole, Alan K. Mackworth: Artificial Intelligence - Foundations of Computational Agents (2e). Cambridge University Press, 2017, ISBN: 9781107195394.
  • Richard S. Sutton and Andrew G. Barto: Reinforcement Learning - An Introduction. MIT Press, 2018, ISBN: 9780262039246.

Lectures:

  • P. Nugues' slides, available here.
  • V. Krueger's slides, available here.
  • E.A. Topp's slides are archived and available via web.archive.org.

Other resources

Companion Jupyter notebook and Python code: