What My Students Are Saying

Course: AI in Radar Signal Processing

Note: This is a review from roughly halfway through the course.

In the course "AI in Radar Signal Processing," Dr. Regev is able to take a complicated cutting edge topic, deliver the background concepts and mathematics quickly yet thoroughly, and then delve into real applications with example code. I work professionally in the automotive radar space and I know that there are many difficult problems still to solve because it's still a new burgeoning technology. The course helped refresh some of the basics, and gave me real applicable mathematical tools to help solve some of these difficult problems using artificial neural networks and other machine learning techniques. I would highly recommend this course for those looking to add some versatile tools to their toolbox if they are working with radar or other similar technologies.

Regards,

—Lucas Wells

I have been working on radar signal processing and target tracking since the second semester of my undergraduate studies, and I am fortunate to study under one of the world's leading experts in these fields. This course provided me with a quick and effective restart in understanding radar systems, particularly in the area of mmWave radar technology.

The mathematical depth and the clear code illustrations are simply perfect, making it an essential resource for anyone looking to advance their skills. If you are an aspiring radar engineer or work in any radar-related field, this course is exactly what you need.
UNQUESTIONINGLY ATTEND THIS COURSE.

—Dayananda B N, Graduate Student, National Institute of Technology Karnataka

Dr. Regev’s AI in Radar Signal Processing course was transformative. The depth of knowledge, paired with real-world applications, helped me grasp complex concepts with ease and helped me with the current projects I’m engaged in. I highly recommend this to anyone serious about advancing their radar signal processing and AI skills.”

—Prem Kumar, Senior Technical Lead in Mercedes Benz, India

The "AI in Radar Signal Processing" course has been a valuable learning experience so far. Dr. Regev breaks down complex topics into manageable concepts, combining the right level of theory with practical examples. The use of example code has been particularly helpful in understanding how AI techniques like neural networks can be applied to real-world radar challenges. This course has already given me new ideas to explore in my work and helped reinforce key radar fundamentals. I’d recommend it to anyone working in radar or looking to expand their skills in this area.

—Pooja Patil, Technical Project Manager for 4D High Resolution Imaging Radar, Greenwave

Dr. Regev's excellent course is well-structured, offering in-depth detail for anyone working in the field. The content is comprehensive, offering valuable theoretical and practical knowledge in radar signal processing. I am very excited to apply the skills I've gained from this course to more advanced radar signal processing projects. I highly recommend this course to both professionals and students interested in radar signal processing and AI.

—Michael Nelson, EE Student at Cal Poly Pomona

Taking the AI in Radar course has been an incredible experience. The content is cutting-edge, focusing on the practical applications of AI to enhance radar systems. Dr. Regev breaks down complex topics into easy-to-understand modules, with very helpful example code . The modules thus far have significantly deepened my understanding of how AI can transform radar technology. I highly recommend this course for anyone looking to advance their knowledge in this evolving field or even looking to recollect radar processing.

—Jakob Herrera, EE Student at Cal Poly Pomona

Top-Rated Engineering Professor at Cal Poly Pomona

📅 Program Schedule

  
      Week
      Focus
      Lecture Duration
    
      1
      CNNs & image classification
      2 hours
    
      2
      Transfer learning on personal data
      2 hours
    
      3
      Object detection with YOLOv5
      2 hours
    
      4
      Detection on video, project showcase
      2 hours

🧠 Tools We Use

Google Colab – no setup required
PyTorch or Keras – flexible industry-grade frameworks
YOLOv5 – state-of-the-art detection model
OpenCV + IPython – for video processing and display

🧪 Capstone Project

Title: Build Your Own AI Detection Tool

Example ideas to choose from:

Detect pets in home videos
Track vehicles in traffic footage
Highlight people in sports/dance videos
Student counter in a classroom

Students Will:

Collect or curate a small dataset (50–100 samples)
Adapt YOLO model and thresholds
Submit a short report + annotated output video

Students receive feedback and a support throughout the Capstone Project.

About Me

Howdy! I'm Dr. Nir Regev — a professor of electrical engineering at Cal Poly Pomona and the founder of AlephZero.ai. Over the years, I’ve had the chance to work on cutting-edge AI and radar projects with companies like Amazon, Google, Flock Safety, Echodyne, ResMed and more.

I specialize in making complex systems understandable and useful, especially in the fields of artificial intelligence, radar and lidar signal processing, and machine learning. More than anything, I love helping students bridge the gap between theory and real-world application. If you're curious, motivated, and ready to build something meaningful, you're in the right place.

Name

First Name

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Who are you applying for?

Which track are you most interested in?

What’s your background in Python or AI?

List any classes, personal projects, or tutorials you’ve done.

What do you hope to gain from this experience?

Be as specific as you like. This helps us personalize your experience.

Have you done any previous camps, clubs, or courses in tech?

Yes

Kind of (YouTube, self-taught, etc.)

Are you able to invest in your learning this summer?

Yes — I’m ready and serious

Possibly — depends on the options

No — not this time

❓ FAQ

Q: What if I miss a live session?
A: All sessions are recorded and posted the same day.

Q: Can beginners join?
A: Yes — as long as you’ve done basic Python (lists, functions, loops).

Q: Can parents attend too?
A: Parents are welcome to watch the intro or project showcases.

Q: Will I get a certificate?
A: Yes — both cohorts receive a signed Certificate of Completion.

Lecture: Understanding Receiver Operating Characteristic (ROC)

Lecture: Peak Interpolation in Radar and Lidar (Snippet)

Bayes rule applied to a COVID-19 rapid test

Mastering the Monty Hall Dilemma: A Reinforcement-Learning Approach

Nehorai & Porat Algorithm (1986)

Quinn & Fernandez Algorithm (1991)

Understanding LoRA — Low Rank Adaptation For Finetuning Large Models