# CS446/ECE449: Machine Learning (Spring 2024)

## Course Information

The goal of machine learning is to develop algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed for a particular task. In this course we will cover three main areas, (1) supervised learning, (2) unsupervised learning, and (3) reinforcement learning. In particular we will cover the following: perceptron, decision trees, Naive Bayes, Gaussian Bayes, linear regression, logistic regression, support vector machines, learning theory, deep learning, structured methods, kMeans, Gaussian mixtures, expectation maximization, VAEs, GANs, Markov decision processes, Q-learning and Reinforce.**Pre-requisites:**Probability, linear algebra, and proficiency in Python.

**Recommended Text:**

(1) Probabilistic Machine Learning: An Introduction [KM]

(2) Deep Learning [GBC]

(3) Understanding Machine Learning: From Theory to Algorithms [SB]

(4) The Elements of Statistical Learning: Data Mining, Inference, and Prediction [HTF]

(5) Deep Learning: Foundations and Concepts [BB]

**Course Deliverables:**

(1) Homework due at 23:59pm, see below for dates

(2) Midterm

(3) Final

**Grading:**

3 credit: Homework 60% (drop 1 homework), Midterm 20%, Final 20%

4 credit: Homework 60% (drop 0 homework), Midterm 20%, Final 20%

Grading policy is subject to change.

**Late Policy:**Each student will have a 3-day grace period in total for the homework assignments. You may use this grace period for any homework assignment throughout the semester. After the grace period, late homework will be penalized 20% per day. Students with legitimate reasons (special accommodations, see below) who contact the professor before the deadline may apply for an extension.

## Instructor & TAs

### Han Zhao

**Instructor**

Email: hanzhao[at]illinois.edu

### Shenlong Wang

**Instructor**

Email: shenlong[at]illinois.edu

### Yubin Ge

**Teaching Assistant**

Email: yubinge2[at]illinois.edu

### Amnon Attali

**Teaching Assistant**

Email: aattali2[at]illinois.edu

### Jing Wen

**Teaching Assistant**

Email: jw116[at]illinois.edu

### Jane Du

**Teaching Assistant**

Email: zd16[at]illinois.edu

## Lectures

Event | Date | Description | Slides | Instructor | Readings |
---|---|---|---|---|---|

Lecture 1 | 01/16/2024 | Introduction, KNN | [Slides] | Han Zhao | |

Lecture 2 | 01/18/2024 | Naive Bayes | [Slides] | Han Zhao | [KM] Chapter 9.3; [BB] Chapter 11.2.4 |

Lecture 3 | 01/23/2024 | Linear Regression | [Slides] | Shenlong Wang | [KM] Chapter 11.1, 11.2 |

Lecture 4 | 01/25/2024 | Logistic Regression | [Slides] | Shenlong Wang | [KM] Chapter 10.1, 10.2 |

Lecture 5 | 01/30/2024 | Support Vector Machine | [Slides] | Han Zhao | [HTF] Chapter 12.2; [SB] Chapter 15.1-15.2 |

Lecture 6 | 02/01/2024 | Kernel Methods | [Slides] | Han Zhao | [SB] Chapter 16.1-16.2; [KM] Chapter 17.3.1-17.3.4 |

Lecture 7 | 02/06/2024 | Decision Trees, Random Forests | [Slides] | Shenlong Wang | [SB] [KM] Chapter 18.1 |

Assignment Due | 02/06/2024 | Assignment 1 Due (23:59 Central Time) | |||

Lecture 8 | 02/08/2024 | Boosting | [Slides] | Shenlong Wang | [SB] [KM] Chapter 18.3, 18.4, 18.5 |

Lecture 9 | 02/13/2024 | PAC Learning Theory (I) | [Slides] | Han Zhao | [SB] Chapter 2.1-2.2 |

Lecture 10 | 02/15/2024 | PAC Learning Theory (II) | [Slides] | Han Zhao | [SB] Chapter 2.3, Chapter 3, 4 |

Lecture 11 | 02/20/2024 | Perceptron Algorithm | [Slides] | Shenlong Wang | |

Assignment Due | 02/20/2024 | Assignment 2 Due (23:59 Central Time) | |||

Lecture 12 | 02/22/2024 | Deep Learning: MLPs, Backpropagation | [Slides] | Shenlong Wang | |

Lecture 13 | 02/27/2024 | Deep Learning: Convolutional Neural Networks | [Slides] | Shenlong Wang | |

Lecture 14 | 02/29/2024 | Deep Learning: Sequential Models, RNNs, LSTMs | [Slides] | Shenlong Wang | |

Lecture 15 | 03/05/2024 | Deep Learning: Attention, Transformers | [Slides] | Shenlong Wang | |

Mid-term Exam | 03/07/2024 | Mid-term Exam | |||

Assignment Due | 03/10/2024 | Assignment 3 Due (23:59 Central Time) | |||

No Class | 03/12/2024 | Spring break | |||

No Class | 03/14/2024 | Spring break | |||

Lecture 16 | 03/19/2024 | K-means | [Slides] | Shenlong Wang | |

Lecture 17 | 03/21/2024 | Gaussian Mixture Models | [Slides] | Shenlong Wang | |

Lecture 18 | 03/26/2024 | Principal Component Analysis / SVD | [Slides] | Shenlong Wang | |

Lecture 19 | 03/28/2024 | Information Theory 101 | [Slides] | Han Zhao | |

Lecture 20 | 04/02/2024 | Auto-encoders / Variational Autoencoders | [Slides] | Shenlong Wang | |

Assignment Due | 04/02/2024 | Assignment 4 Due (23:59 Central Time) | |||

Lecture 21 | 04/04/2024 | Generative Adversarial Networks | [Slides] | Han Zhao | |

Lecture 22 | 04/09/2024 | Diffusion Models / Score Matching | [Slides] | Han Zhao | |

Lecture 23 | 04/11/2024 | Self-supervised Learning / Contrastive Learning | [Slides] | Han Zhao | |

Assignment Due | 04/11/2024 | Assignment 5 Due (23:59 Central Time) | |||

Lecture 24 | 04/16/2024 | Language Modeling / Foundation Models | [Slides] | Han Zhao | |

Lecture 25 | 04/18/2024 | Markov Decision Process | [Slides] | Han Zhao | |

Lecture 26 | 04/23/2024 | Q-Learning | [Slides] | Han Zhao | |

Lecture 27 | 04/25/2024 | Policy Gradient | [Slides] | Han Zhao | |

Lecture 28 | 04/30/2024 | Course Review | [Slides] | Han Zhao | |

Assignment Due | 04/30/2024 | Assignment 6 Due (23:59 Central Time) | |||

Exam | TBD | Final Exam |