# Introduction to Machine Learning

**University of California, Berkeley**, Fall 2023

Welcome to CS 189/289A! This class covers theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; deep learning models including CNNs, Transformers, graph neural networks for vision and language tasks; and Markovian models for reinforcement learning and robotics.

Here are the Gradescope/Ed codes (you should self-enroll in these). We won’t post any materials on bCourses.

- Gradescope: E73744
- Ed: https://edstem.org/us/join/fCBF32

Also, the Professors will post slides prior to lecture at this Google Drive folder (for faster access). The material here is redundant with the website, but it may take up to a day or two for the website to get updated with the slides after lecture. The “Post-Lecture” subfolder contains updates to slides that the Professors may make right after lecture.

**Note:** The topics for future lectures, discussions, and HWs are tentative and may be moved around, changed, or removed.

## Overview

### Week 1

8/24 | Lecture 1: Introduction and Logistics | Slides |

### Week 2

8/28 | Homework 1: Math Review (Due 9/8 11:59pm) | |

8/29 | Lecture 2: Maximum Likelihood Estimation | Slides |

8/30 | Discussion 0: Math Pre-Requisites Review | PDF, Solutions |

8/31 | Lecture 3: Multivariate Gaussians | Slides |

### Week 3

9/5 | Lecture 4: Linear Regression 1 | Slides |

9/6 | Discussion 1: MLE & Gaussians | PDF, Solutions |

9/7 | Lecture 5: Linear Regression 2 | Slides |

9/9 | Homework 2: Linear/Logistic Regression & Classification (Due 9/22 11:59pm) |

### Week 4

9/12 | Lecture 6: Classification - Generative & Discriminative | Slides |

9/13 | Discussion 2: Linear Regression | PDF, Solutions |

9/14 | Lecture 7: Logistic Regression & Neural Networks | Slides |

### Week 5

9/19 | Lecture 8: Backpropagation and Gradient Descent 1 | Slides |

9/20 | Discussion 3: Classification & Logistic Regression | PDF, Solution, Walkthrough |

9/21 | Lecture 9: Backpropagation and Gradient Descent 2 | Handout |

9/23 | Homework 3: Neural Networks (Due 10/6 11:59pm) | PDF, Files |

### Week 6

9/26 | Lecture 10: Neural Networks - CNNs, Batch Norm, & ResNets | Slides |

9/27 | Discussion 4: Neural Networks and Training | |

9/28 | Lecture 11: Neural Netowkrs - Attention & Transformers |

### Week 7

10/3 | Lecture 12: Dimensionality Reduction & PCA | |

10/4 | Discussion 5: Convolution and Attention | |

10/5 | Lecture 13: t-SNE | |

10/7 | Homework 4: Dimensionality Reduction & Decision Theory (Due 10/20 11:59pm) |

### Week 8

10/10 | Lecture 14: Clustering | |

10/11 | Discussion 6: Dimensionality Reduction Techniques | |

10/12 | Lecture 15: Multiway Classification, Decision Theory, & Model Evaluation | |

10/13 | Midterm (7-9pm, Pimentel 1) |

### Week 9

10/17 | Lecture 16: Bias-Variance Tradeoff & Over/Under-Fitting | |

10/18 | Discussion 7: Clustering and Decision Theory | |

10/19 | Lecture 17: Nearest Neighbors & Metric Learning | |

10/21 | Homework 5: Bias/Variance, Nearest Neighbors, Decision Trees (Due 11/3 11:59pm) |

### Week 10

10/24 | Lecture 18: Decision Trees & Ensembling | |

10/25 | Discussion 8: Bias/Variance and Nearest Neighbors | |

10/26 | Lecture 19: Hidden Markov Models & Graphical Models 1 |

### Week 11

10/31 | Lecture 20: Hidden Markov Models & Graphical Models 2 | |

11/1 | Discussion 9: Decision Trees and HMMs Intro | |

11/2 | Lecture 21: Markov Decision Processes | |

11/4 | Homework 6: Markovian Models & Reinforcement Learning (Due 11/17 11:59pm) |

### Week 12

11/7 | Lecture 22: Reinforcement Learning | |

11/8 | Discussion 10: HMMs Advanced and MDPs | |

11/9 | Lecture 23: Robotics and Machine Learning |

### Week 13

11/14 | Lecture 24: Language and Vision Applications | |

11/15 | Discussion 11: Robotics, Language, & Vision | |

11/16 | Lecture 25: Graph Neural Networks & Rotational Equivariance 1 | |

11/18 | Homework 7: Graph Neural Networks & Applications of Deep Learning (Due 12/1 11:59pm) |

### Week 14

11/21 | Lecture 26: Graph Neural Networks & Rotational Equivariance 2 | |

11/22 | Discussion 12: Graph Neural Networks | |

11/23 | No Lecture (Thanksgiving) |

### Week 15

11/28 | Lecture 27: Special Topics - Computational Biology | |

11/30 | Lecture 28: Special Topics - Causality |

### Week 16 (RRR Week)

No lectures or discussions this week |

### Week 17 (Finals Week)

12/12 | Final Exam (8-11am, Location TBD) |