Foundations for Machine Learning
LINEAR ALGEBRA I CALCULUS I STATISTICS I DATA STRUCTURES I
Author of Deep Learning Illustrated
Consists of 14-part ON-DEMAND 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, Untap. 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 modules)
2. Calculus Course (4 modules)
3. Probability and Statistics Course (4 modules)
4. Computer Science (3 modules)
5. Matrix Operations for Machine Learning
2. Computing Derivatives with Differentiation
3. Automatic Differentiation
4. Gradients Applied to Machine Learning
1. Introduction to Probability
2. Distribution in Machine Learning
3. Information Theory
4. Frequentist Statistics
6. Bayesian Statistics
1. Introduction to Data Structures and Algorithms
2. Lists and Dictionaries
3. Trees and Graphs
4. The Machine Learning Approach to Optimization
5. Gradient Descent
6. Fancy Deep Learning Optimizers
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.