I am a self-driving car engineer, interested in self-driving car technology and machine learning.
Over the past few years, I've completed 45 courses and got certifications using my spare time. I recognize the importance of lifelong learning and want to achieve self-growth through continuous learning.
Some completed courses and certifications are shown in the table below, including self-driving car, robotics, machine learning, and data science. For more courses, you can view courses.
| Catigory | Course Name / Certification | University / Educational Service |
|---|---|---|
| Self Driving Car | Self-Driving Car Engineer | Udacity |
| Self Driving Car | Sensor Fusion | Udacity |
| Self Driving Car | Introduction to Self-Driving Cars | University of Toronto |
| Self Driving Car | State Estimation and Localization for Self-driving Cars | University of Toronto |
| Self Driving Car | Visual Perception for Self-Driving Cars | University of Toronto |
| Self Driving Car | Motion Planning for Self-Driving Cars | University of Toronto |
| Self Driving Car | Self-Driving Cars Specialization | University of Toronto |
| Robotics | Robotics: Perception | University of Pennsylvania |
| Machine Learning | Deep Learning | Udacity |
| Machine Learning | Machine Learning | Stanford University |
| Machine Learning | Mining Massive Datasets | Stanford University |
| Machine Learning | Probabilistic Graphical Models 1: Representation | Stanford University |
| Machine Learning | Machine Learning Foundations | National Taiwan University |
| Machine Learning | Machine Learning Techniques | National Taiwan University |
| Machine Learning | Introduction to Big Data | University of California San Diego |
| Machine Learning | Machine Learning Foundations: A Case Study Approach | University of Washington |
| Machine Learning | Machine Learning: Regression | University of Washington |
| Machine Learning | Machine Learning: Classification | University of Washington |
| Machine Learning | Machine Learning: Clustering Retrieval | University of Washington |
| Machine Learning | Machine Learning: Specialization | University of Washington |
| Data Science | Introduction to Big Data with Apache Spark | University of California Berkeley |
| Data Science | Scalable Machine Learning | University of California Berkeley |
| Data Science | Business Metrics for Data-Driven Companies | Duke University |
| Data Science | Data Analysis and Statistical Inference | Duke University |
| Data Science | Data Visualization and Communication with Tableau | Duke University |
| Data Science | The Analytics Edge | Massachusetts Institute of Technology |
| Data Science | Exploratory Data Analysis | Johns Hopkins University |
| Data Science | Getting and Cleaning Data | Johns Hopkins University |
| Data Science | R Programming | Johns Hopkins University |
| Data Science | Using Python to Access Web Data | University of Michigan |
| Data Science | Using Databases with Python | University of Michigan |
| Data Science | Big Data, Cloud Computing, & CDN Emerging Technologies | Yonsei University |
| Data Science | Internet Emerging Technologies | Yonsei University |
I've completed many interesting projects through learning courses. Some of my projects are:
| Project | Description |
|---|---|
| System Integration | Implement with ROS the main modules of an autonomous vehicle: Perception, Planning and Control, which will be tested on Udacity´s Self Driving Car ´Carla´ around a test track using waypoint navigation. |
| Path Planning | Develop decision making, path planning algorithms to enable vehicles to keep lanes and change lanes |
| Motion Planning | Implement behavioral planning logic, as well as static collision checking, path selection to make car avoid both static and dynamic obstacles while tracking the center line of a lane, while also handling stop signs. |
| Object Tracking using EKF | Utilize sensor data from both LIDAR and RADAR measurements for object (e.g. pedestrian, vehicles, or other moving objects) tracking with the Extended Kalman Filter. |
| Object Tracking using UKF | Utilize sensor data from both LIDAR and RADAR measurements for object (e.g. pedestrian, vehicles, or other moving objects) tracking with the Unscented Kalman Filter. |
| Feature Tracking | Build the feature tracking part and test various detector / descriptor combinations to see which ones perform best. |
| 3D Object Tracking | Calculate TTC using lidar and camera |
| Visual Odometry for Localization in Autonomous Driving | Visual odometer including feature detection, feature drecription, feature matching, motion estimation through pnp |
| Environment Perception For Self-Driving Cars | drivable space estimation in 3D, lane estimation |
| CS231N Homeworks | Implement forward and backward process of neural network, including CNN, RNN, LSTM |
| Face Generation | Use a DCGAN on the CelebA dataset to generate images of novel and realistic human faces. |
| Machine Translation | Train a sequence to sequence network for English to French translation. |
| Reinforcement Learning (Q-Learning) | Implement a deep Q-learning network to play a simple game from OpenAI Gym. |
To learn more about the projects，you can check these repositories:
| Repository | Description |
|---|---|
| Self-Driving Cars Specialization | Self driving car specialization taught in Coursera by University of Toronto |
| Sensor Fusion NanoDegree | Sensor Fusion nano-degree taught by Udacity. |
| Self Driving Car NanoDegree | Self driving car nano-degree taught by Udacity. |
| Deep Learning NanoDegree | Deep learning nano-degree taught by Udacity. |
| Robotics | Robotics Course taught by University of Pennsylvania. |
| Projects collection 1 | Other related projects. |
| Projects collection 2 | Other related projects. |