Foundations for Machine Learning: Mini Bootcamp
LINEAR ALGEBRA I CALCULUS I STATISTICS I DATA STRUCTURES I
Consists of 14-part LIVE training modules, this course provides a comprehensive overview of all of the subjects --across mathematics, statistics, and computer science --that underlie contemporary machine learning approaches, including deep learning and other artificial intelligence techniques.
If you use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow, PyTorch) to train or deploy machine learning algorithms, and would like now to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities.
Jon Krohn is Chief Data Scientist at the machine learning company, Untapt. He authored the 2019 book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy. Jon holds a Ph.D. in Neuroscience from Oxford and has been publishing on machine learning In leading academic journals since 2010; his papers have been cited over a thousand times.
1. Linear Algebra Course (3 live modules)
- December 3, 10 and 17
2. Calculus Course (4 live modules)
- January 13 and 27, February 10 and 24
3. Probability and Statistic Course (4 live modules)
- March 2021
4. Computer Science (3 live modules)
- Spring 2021
3. Automatic Differentiation
4. Gradients Applied to Machine Learning
5. Integrals
Programming: All code demos will be in Python, so experience with it or another object-oriented programming language would be helpful for following along with the code examples.
Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information -- such as understanding charts and rearranging simple equations -- then you should be well prepared to follow along with all the mathematics.