**Table of Content**

1. Basic Information

2. Highlights

3. Course Description

4. Assignments

5. Additional Course Information

## 1. Basic Information

- Instructor: Yangfeng Ji (Office hour: Friday 9 AM, Rice 510 and Zoom)
- Semester: Spring 2023
- Location: Rice 130
- Time: Tuesday and Thursday 11:00 AM – 12:15 PM
- TA:

## 2. Highlights

## 3. Course Description

- The goal of this course is to
**understand**basic concepts and algorithms in machine learning. - Teaching how to use machine learning packages (e.g., sklearn) will not be a focus of this course. Although, we do have demos built upon these packages for explaining the concepts and algorithms.
- Deep learning (or neural network modeling) will only be a small part of this course.

### 3.1 Topics

Most of the course materials (and assignments) are adopted from Shalev-Shwartz and Ben-David’s textbook on machine learning. Particularly, the topics overed in this course are:

- Introduction to learning theory
- Linear classification and regression
- Support vector machines and kernel methods
- Model selection and validation
- Neural networks and deep learning
- Optimization
- Generative modeling

For more information about this course, please checkout the [schedule](schedule.

### 3.2 Prerequisites

In addition to the programming skill, basic probability theory and linear algebra are highly recommended.

- Programming and Algorithm: CS 2150 or CS 3100 with a grade of C- or better
- We will use Python for our homework assignments

- Probability: APMA 3100, APMA 3110, MATH 3100, or equivalent
- Example topics: definition of probability, basic probability distributions (e.g., Gaussian, Bernoulli, and Multi-nominal)

- Linear Algebra: Math 3350 or APMA 3080 or equivalent
- Example topics: matrix-vector multiplication, special metrices, linear transformation, matrix factorization (e.g., SVD)

### 3.3 Textbook

- [UML] Shalev-Shwartz and Ben-David. Understanding Machine Learning: From Theory to Algorithms. 2014
- [DL] Goodfellow, Bengio, and Courville. Deep Learning. 2016

**Additional**

- [PRML] Bishop. Pattern Recognition and Machine Learning. 2006
- [MRT] Mohri, Rostamizadeh, and Talwalkar. Foundations of Machine Learning. 2nd Edition. 2018

## 4. Assignments

There are three parts of assignment

- Homework (70 points)
- There are five assignments, each of them will have about two weeks to finish. Students can find the tentative release dates of the homework assignments on the schedule page
- Each homework is worth 14 points
- Students should work independently on homework assignments

- Quiz (10 points)
- There are ten quizzes in total, each of them is worth 1 point
- The quiz will be distributed during lecture
- participation is more important than correctness of the answer

- Final project (20 points)
- There will be several pre-defined projects and students will select one of them for their final project
- Students need to form groups for their final project, the size of each group should be 3 – 4 students
- More details about final project will be released soon

### 4.1 Grading Policy

For more information about grading policy, please visit this page.

## 5. Additional Course Information

For the information about the honor code and other course policies, please visit this page.