Artificial Intelligence Courses

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Introduction to Python: I. Basic Programming

Our introductory coding course in Python is a departure from tedious legacy programming exercises. In this course, students will develop a solid foundation in coding for Scientific Computing and AI applications through our modern, streamlined lectures to pursue higher-level applications in Data Science, Machine Learning, and Robotics.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: N/A

Lecture 1: Introduction to Computer Programming 

Lecture 2: Python Numeric Variable Types 

Lecture 3: Strings and Text Input/Output

Lecture 4: Lists

Lecture 5: Conditions and Loops

Lecture 6: Functions

Lecture 7: Tuples and Dictionaries

Lecture 8: Sets and Hashing

Lecture 9: Classes and Object-Oriented Programming I 

Lecture 10: Classes and Object-Oriented Programming II

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Introduction to Python: II. Data Structures and Algorithms

Students who have mastered basic programming skills will advance to study classic data structures and computer algorithms widely used in developing solutions in AI, Data Science, and Robotics.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 1-1

Lecture 1: Basic Data Structure

Lecture 2: Debugging Skills

Lecture 3: Sorting Algorithms

Lecture 4: Queues and Breadth-First Search 

Lecture 5: Stacks and Depth-First Search 

Lecture 6: Priority Queues and A* Search

Lecture 7: Tree Structure

Lecture 8: File I/O

Lecture 9: Dynamic Programming I 

Lecture 10: Dynamic Programming II

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Python Scientific Programming

Students will learn how to perform complex data analysis and visualization, as well as basic regression and classification in linear/nonlinear problems.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 1-1, 1-2

Lecture 1: Numpy

Lecture 2: Visualization

Lecture 3: Vectors and Matrices I

Lecture 4: Vectors and Matrices II 

Lecture 5: Linear Regression I 

Lecture 6: Linear Regression II 

Lecture 7: Gradient Descent I 

Lecture 8: Gradient Descent II 

Lecture 9: Classification I

Lecture 10: Classification II

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Learning Computer Vision in Python

Many modern AI theories are derived from studying computer vision problems, as the vision system is the most sophisticated perception system in the human brain. This course will teach undergraduate-level computer vision topics commonly offered at leading university engineering programs.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 1-1, 1-2, 2-1

Lecture 1: Introduction to CV 

Lecture 2: 3D Rigid-Body Motion 

Lecture 3: Imaging

Lecture 4: Image Features 

Lecture 5: Adaptive Algorithms 

Lecture 6: Object Detection 

Lecture 7: Cascade Classifiers 

Lecture 8: Object Tracking

Lecture 9: Localization

Lecture 10: 3D Sensing

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Introduction to Deep Learning

Students who have mastered the foundation of scientific computing and computer vision will learn the modern approach of Deep Learning in Machine Learning. This course covers basic single- to multi-layer perceptron models, deep convolutional networks, and reinforcement learning in decision-making and games.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 2-1, 2-2

Lecture 1: Neural Networks and Perceptrons 

Lecture 2: Multi-Layer Perceptrons

Lecture 3: Convolutional Neural Networks 

Lecture 4: Deeper Neural Networks

Lecture 5: Fine Tuning and Transfer Learning 

Lecture 6: ResNet Classification

Lecture 7: Reinforcement Learning I

Lecture 8: Reinforcement Learning II

Lecture 9: Reinforcement Learning III 

Lecture 10: Reinforcement Learning IV

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Introduction to Robotics and Autonomous Driving

This course offers an introductory robotics course with a focused application in teaching autonomous driving practices. The content of this course is comparable to upper-division courses in leading university engineering programs.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 2-1, 2-2, 3-1

Lecture 1: Introduction to Robotics and Automation 

Lecture 2: Basic Vehicle Mechanical and Dynamic Models 

Lecture 3: PID Control

Lecture 4: Lane Following

Lecture 5: Collision Detection and Avoidance

Lecture 6: Behavior Mimicking using DNN Models

Lecture 7: Training Controllers via Reinforcement Learning 

Lecture 8: Training Controllers via Reinforcement Learning 

Lecture 9: Tuning Autopilots in CARLA Simulator

Lecture 10: Tuning Autopilots on RC Model Vehicles

Metaverse Master Class​

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Learning Unity Game Engine

By the end of this course, students new to Metaverse programming will be able to make, produce, and build their own 2D/3D projects. It will cover essential functions of Unity Game Engine, including Unity interface, GameObject components, entry-level C# scripting, interactive game audio, and importing 2D / 3D art elements (Meshes / Sprites).

Instructor(s): Eddy Hu

Total Lecture Time: 10 lectures

Prerequisites: N/A

Lecture 1: Introduction to Computer Programming 

Lecture 2: Python Numeric Variable Types 

Lecture 3: Strings and Text Input/Output

Lecture 4: Lists

Lecture 5: Conditions and Loops

Lecture 6: Functions

Lecture 7: Tuples and Dictionaries

Lecture 8: Sets and Hashing

Lecture 9: Classes and Object-Oriented Programming I 

Lecture 10: Classes and Object-Oriented Programming II

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Unity Programming

This course introduces the fundamentals of C# programming in Unity Game Engine. From Physic engine elements such as Collider, Trigger, and Rigidbody to the concept of instantiating 3D models and bullets in the game scene, the students will learn the variety of tools essential for developing Unity Interactive projects. Students will develop a playable demo as the final assignment of this course.

Instructor(s): Eddy Hu

Total Lecture Time: 10 lectures

Prerequisites: 2-3

Lecture 1: Basic Data Structure

Lecture 2: Debugging Skills

Lecture 3: Sorting Algorithms

Lecture 4: Queues and Breadth-First Search 

Lecture 5: Stacks and Depth-First Search 

Lecture 6: Priority Queues and A* Search

Lecture 7: Tree Structure

Lecture 8: File I/O

Lecture 9: Dynamic Programming I 

Lecture 10: Dynamic Programming II

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Foundation of AR/VR and Metaverse

This course will immerse learners in the application of AR/VR and Metaverse. The lectures will assume learners have gained basic knowledge of computer vision (2-2) & gaming programming (2-4), and will introduce the science behind immersive 3D human perception and how modern wearable technologies may accurately stimulate human 3D perception using sensors and displays. The course lays the foundation for the learners to develop future metaverse applications.

Instructor(s): Dr. Allen Yang

Total Lecture Time: 10 lectures

Prerequisites: 2-2, 2-3, 2-4

Lecture1: Introduction to AR/VR

Lecture2: Human Perception of Reality

Lecture3: Near-Eye Display Technologies

Lecture4: Rigid-Body Motion

Lecture5: Cameras and Imaging

Lecture6: Depth Cameras

Lecture7: AR Localization

Lecture8: Human Avatar Creation

Lecture9: Experiment I: Build a Metaverse in Unity3D

Lecture10: Experiment II: Build a Metaverse in Unity3D

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Creative Design of 3D World - Next Gen Character Creation

In this course, students will learn in-depth techniques for modeling, texturing, and rendering a cutting-edge real-time character. The class will work similarly to a live mentorship as students approach the creation of AAA game characters for their portfolios. Students should expect to cover head & hair, costume elements, low-poly UVs, and processing required to get the asset real-time ready, and finish with material and texture creation to set up the final model in the engine with final images.

Instructor(s): Mikki Xu

Total Lecture Time: 10 lectures

Prerequisites: 2-3

Lecture 1: Introduction to CV 

Lecture 2: 3D Rigid-Body Motion 

Lecture 3: Imaging

Lecture 4: Image Features 

Lecture 5: Adaptive Algorithms 

Lecture 6: Object Detection 

Lecture 7: Cascade Classifiers 

Lecture 8: Object Tracking

Lecture 9: Localization

Lecture 10: 3D Sensing

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