**EECS 453 Principles of Machine Learning**

**Course** **Instructor:** Prof. Qing Qu

**Course Time:** Mon/Wed 12:00 PM – 1:30 PM, 3 credit hour

**Office Hour**: Wed 3:30 PM – 5:00 PM

**Prerequisite:** EECS 351, or EECS 301, or any linear algebra courses

**Notice:** This is an entry-level ECE machine learning course targeted for senior EE & CE undergraduate, and junior master students outside SIPML area. All students outside EECS that want to learn the basics of ML are also welcome! **Compared to EECS 445, this course places slightly greater emphasis on mathematical principles and is better suited for students who have limited experience with programming and machine learning.**

**Overview**: The class will cover basic principles in machine learning, such as unsupervised learning (e.g., clustering, mixture models, dimension reduction), and supervised learning (e.g., regression, classification, neural networks & deep learning). For each topic, key algorithmic ideas/intuitions and basic theoretical insights will be highlighted.

**Course Materials:** slides and videos will be accessed via Canvas (TBA). Tentative topics that will be covered in this course are **supervised learning, unsupervised learning, and reinforcement learning**:

- Basics of probability, linear algebra, and optimization
- Regression and linear prediction
- Support vector machines and kernel methods
- Deep neural networks
- Dimension reduction: PCA, autoencoder
- Clustering (Kmeans, Mixture of Gaussians, EM)
- Representation learning: nonnegative matrix factorization, dictionary learning

**Assessment**: (i) 5 homework assignments (40%), (ii) mid-term exam (30%), (iii) course projects (25%), (iv) participation & course evaluation (5%)

Assessment | Percentage |

Homework (5) | 40% |

Midterm Exam | 30% |

Projects | 25% |

Participation & Course Evaluation | 5% |

**Textbook:** We recommend the following books and articles, although we will not follow them closely.

- Foundations of Machine Learning, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
- Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
- Linear Algebra and Optimization for Machine Learning, by Charu C. Aggarwal.

**Related courses**:

- EECS 445. Introduction to Machine Learning
- EECS 453. Applied Matrix Algorithms for Signal Processing, Data Analysis, and Machine Learning
- EECS 505. Computational Data Science and Machine Learning
- EECS 545. Machine Learning

**Course Syllabus** (**Note:** the schedule is tentative, and is subject to change during the semester.)

Week | Date | Topic | Contents | Homework, Review |

Week-1-1 | 08/29 | Introduction (Remote) | Course overview | |

Week-1-2 | 08/31 | Supervised Learning (Remote) | Introduction to supervised learning, linear models, regularization | Linear Algebra Review |

Week-2-1 | 09/05 | Labor Day | No class | |

Week-2-2 | 09/07 | Supervised Learning | Learning Theory | Probability Review, HW1 Release |

Week-3-1 | 09/12 | Supervised Learning | Linear regression I | |

Week-3-2 | 09/14 | Supervised Learning | Linear regression II | Python Review |

Week-4-1 | 09/19 | Supervised Learning | Linear Classifiers | |

Week-4-2 | 09/21 | Supervised Learning | Linear Discriminant Analysis | HW1 Due, HW2 Release |

Week-5-1 | 09/26 | Supervised Learning (remote) | Logistic regression | |

Week-5-2 | 09/28 | Supervised Learning (remote) | Optimization methods I | |

Week-6-1 | 10/03 | Supervised Learning | Optimization methods II | |

Week-6-2 | 10/05 | Supervised Learning | Support vector machine (SVM) I | HW2 Due, HW3 Release |

Week-7-1 | 10/10 | Supervised Learning | Support vector machine (SVM) II | |

Week-7-2 | 10/12 | Supervised Learning | Support vector machine (SVM) III | |

Week-8-1 | 10/17 | Fall Study Day | No class | |

Week-8-2 | 10/19 | Supervised Learning | Dual SVM | HW3 Due |

Week-9-1 | 10/24 | Supervised Learning | Nonlinear models, kernel methods | |

Week-9-2 | 10/26 | Supervised Learning | Introduction to deep neural networks I | |

Week-10-1 | 10/31 | Supervised Learning | Introduction to deep neural networks II | |

Week-10-2 | 11/02 | Supervised Learning | Introduction to deep neural networks III | |

Week-11-1 | 11/07 | Midterm | Midterm | |

Week-11-2 | 11/09 | Unsupervised Learning | Introduction to unsupervised learning, clustering problem, K-means | Project Proposal Due, HW4 Release |

Week-12-1 | 11/14 | Unsupervised Learning | K-means, mixtures of Gaussian, expectation maximization | |

Week-12-2 | 11/16 | Unsupervised Learning | Dimension reduction, PCA | |

Week-13-1 | 11/21 | Unsupervised Learning | Dimension reduction II | |

Week-13-2 | 11/23 | Thanksgiving | No Class | |

Week-14-1 | 11/28 | Unsupervised Learning (Remote) | Representation learning, matrix factorization | HW4 Due, HW5 Release |

Week-14-2 | 11/30 | Unsupervised Learning (Remote) | Autoencoder & self-supervised learning | |

Week-15-1 | 12/05 | Unsupervised Learning | Generative Models | HW5 Due |

Week-15-2 | 12/07 | Final Presentation |