1 | Probability review | Lecture 1 | Lists, Tuples and Dictionaries |

2 | Information theory | Lecture 2 | Iterations and Error Handling |

3 | Data Preprocessing | Lecture 3 | Data Preprocessing |

4 | k-nearest neighbors | Lecture 4 | |

5 | Naive Bayes | Lecture 5 | |

6 | Decision Trees and Random Forests | Lecture 6 | Grid Search Cross-Validation |

7 | Support vector machines | Lecture 7 | |

8 | AdaBoost and Gradient Boosting Machines | Lecture 8 | SVMs and Boosted Trees |

9 | Clustering and Gaussian Mixture Models | Lecture 9 | HuggingFace Spaces |

10 | Neural Networks | Lecture 10 | Intro to PyTorch |

11 | Convolutional neural networks | Lecture 11 | CNNs |

12 | Recurrent neural networks | Lecture 12 | LSTMs |

13 | Transformers | Lecture 13 | Transformers |

14 | Generative Adversarial Networks and Diffusion Models | Lecture 14 | DCGANs |