Applied Machine Learning Intensive: From Python to Deep Neural Networks
CSE-90216
This intensive 10-week program combines three foundational Machine Learning courses (Python and Mathematics for Machine Learning - CSE-90162, Machine Learning Algorithms - CSE-90160, Deep Neural Networks - CSE-90161) into a comprehensive accelerated experience. Students build proficiency in Python and master essential mathematics (statistics, linear algebra, calculus). These skills are applied to implement ML algorithms including regression, classification, clustering, and dimension reduction. Students then build a Deep Neural Network framework from scratch and apply it to complex classification and image processing tasks.
What you'll learn
- Create Python programs in the Google Colaboratory or Jupyter Notebooks environment using standard Machine Learning libraries (NumPy, Pandas, Matplotlib) to preprocess, analyze, and visualize datasets.
- Apply mathematical foundations, including statistics, linear algebra, and introductory calculus to the formulation and analysis of Machine Learning models.
- Implement and evaluate supervised learning algorithms, including regression models and classification techniques such as Naive Bayes and K-nearest neighbors (KNN).
- Implement and evaluate unsupervised learning algorithms, including clustering (K-means, DBSCAN) and dimension reduction techniques (PCA, LDA).
- Design, implement, and test a Deep Neural Network (DNN) framework from scratch in Python, incorporating forward propagation, backpropagation, and optimization techniques.
- Write and test working Python programs from a generic problem statement through algorithm development, design, and implementation, unit test, integration, and final test.
Course Information
Students may begin this course up to two weeks after the official start date. Students who begin on the official start date will have the option to complete the course two weeks prior to the official end date.
Payment plans are available - please contact precollege@ucsd.edu for more information.
Course sessions
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
Python and Math Essentials for Machine Learning - a Beginner’s Guide 1st
by Mauro, Anthony
ISBN / ASIN: 9798350948486
Artificial Neural Networks: A Beginner's Guide 1st
by Mauro, Anthony
ISBN / ASIN: 9798350959086
You may purchase textbooks via the UC San Diego Bookstore.
Policies:
- No refunds after: 6/1/2026
- Early enrollment advised
- No UCSD parking permit required
- No visitors permitted
- Pre-enrollment required
Schedule:
Instructor:
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Anthony Mauro
His formal education is in Electrical Engineering, where he completed his BSEE and MSEE degrees from California Polytechnic University and the University of Southern California. He worked as a hardware, software, and systems design engineer at Qualcomm Inc. for over 20 years, where he was awarded over 20 patents. He joined the faculty at UC San Diego in 2022, where he develops curriculum and teaches in the Division of Extended Studies and Futures.
He is also active in computer science and engineering pathways with the California Career Technical Education (CTE) program of study and contributes to the Institute of Electrical and Electronics Engineers (IEEE) to promote the fields to secondary students.