# CS446/ECE449: Machine Learning (Spring 2022)

## Course Information

The goal of Machine Learning is to find structure in data. In this course we will cover three main areas, (1) discriminative models, (2) generative models, and (3) reinforcement learning models. In particular we will cover the following: linear regression, logistic regression, support vector machines, deep nets, structured methods, learning theory, 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) Machine Learning: A Probabilistic Perspective by Kevin Murphy, (2) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, (3) Pattern Recognition and Machine Learning by Christopher Bishop, (4) Graphical Models by Nir Friedman and Daphne Koller, (5) Reinforcement Learning by Richard Sutton and Andrew Barto, (6) Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David

**Course Deliverables:**

(1) Homework

**due at noon, see below for dates (no late submission accepted)**

(2) Midterm

(3) Midterm 2

**Grading:**

3 credit: 60% homework (drop 1 homework), 20% midterm, 20% midterm 2

4 credit: 60% homework (drop 0 homework), 20% midterm, 20% midterm 2

Grading policy is subject to change.

**TA/Office Hours:**

Please see calendar on this [link]

**Midterm 2:**May 03 2022 during regular class time (attendance mandatory)

## Instructor & TAs

### Class Time & Location

Class Time: Tuesday, Thursday 12:30PM-1:45PM (hybrid/online; see campuswire for link)## Lectures

The syllabus is subject to change.

Event | Date | Description | Slides | Recording | Material | Format |
---|---|---|---|---|---|---|

Lecture 1 | 01/18/2022 | Overview; start of linear regression | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 2 | 01/20/2022 | Linear Regression | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 3 | 01/25/2022 | Logistic Regression | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 4 | 01/27/2022 | Linear prediction: features, overfitting, and losses | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 5 | 02/01/2022 | Convex optimization | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 6 | 02/03/2022 | Support Vector Machines 1 | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 7 | 02/08/2022 | Support Vector Machines 2 | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

HW | 02/08/2022 | Homework 1 (due at noon) | [HW] | online | ||

Lecture 8 | 02/10/2022 | Deep Nets 1 | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 9 | 02/15/2022 | Pytorch Tutorial | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 10 | 02/17/2022 | Deep Nets 2 | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 11 | 02/22/2022 | Nearest Neighbors and decision trees | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

HW | 02/22/2022 | Homework 2 (due at noon) | [HW] | online | ||

Lecture 12 | 02/24/2022 | Ensemble methods | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 13 | 03/01/2022 | Learning theory | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

Lecture 14 | 03/03/2022 | Review | [Slides] [Slides Split] [Slides Annot] | [Rec] | hybrid | |

HW | 03/03/2022 | Homework 3 (due at noon) | [HW] | online | ||

Midterm | 03/08/2022 | Midterm | online | |||

Lecture 15 | 03/10/2022 | PCA | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Break | 03/15/2022 | Break | online | |||

Break | 03/17/2022 | Break | online | |||

Lecture 16 | 03/22/2022 | k-Means | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 17 | 03/24/2022 | Gaussian Mixture Models | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 18 | 03/29/2022 | Expectation Maximization | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 19 | 03/31/2022 | Variational Auto-Encoders | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 20 | 04/05/2022 | Generative Adversarial Nets | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

HW | 04/05/2022 | Homework 4 (due at noon) | [HW] | online | ||

Lecture 21 | 04/07/2022 | Autoregressive Methods | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 22 | 04/12/2022 | Transformers | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

HW | 04/14/2022 | Homework 5 (due at noon) | [HW] | online | ||

Lecture 23 | 04/14/2022 | Graph Neural Nets | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 24 | 04/19/2022 | MDP | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 25 | 04/21/2022 | Q-Learning | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Lecture 26 | 04/26/2022 | Actor-Critic & Policy Gradient | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

HW | 04/26/2022 | Homework 6 (due at noon) | [HW] | online | ||

Lecture 27 | 04/28/2022 | Review | [Slides] [Slides Split] [Slides Annot] | [Rec] | online | |

Midterm 2 | 05/03/2022 | Midterm 2 | online |