Statistical Machine Learning · Statistics
Unsupervised Learning algos, regression, lasso, ridge, non-parametric kernel methods, classification, theory in ML.
A laundry list of important machine learning topics, really difficult to name all of them because every class covers something new.
General ML knowledge and all the skills that I can apply into real world problems
This course was outstanding. Having taken a previous introductory AI course, this one delved deeply into the mathematical models and intuitions behind common ML models, including k-NN, various classifiers, neural networks, SVMs, Gaussian mixtures, and more. It certainly demands a solid math background to keep up; otherwise, you'll need to invest a lot of time and effort to get up to speed.
The class is much smaller than the COMS ML course, which lends itself better to asking more questions, and allows the prof to go at a better pace. more rigorous and theoretical than the COMS course and overall i think it was a solid introduction to machine learning topics.
Overview of many ML algorithms and the mathematical basis behind them
This course was extremely satisfying and left me eager for more. Professor Samory ensures that the class grasps the concepts before moving on, and he is always willing to provide intuition or any other explanations needed. While he may initially seem slightly intimidating due to his mastery of the material and sharp intellect, it all becomes worthwhile as you get accustomed to his teaching style. This was truly my favorite class of the semester. 1 Columbia University: Arts & Sciences Spring 2024 Course: STATGU4241_001_2024_1-STATISTICALMACHINELEARNING : STATGU4241_001_2024_1 - STATISTICAL MACHINE LEARNING Instructor: Samory Kpotufe
A good range of ML topics, but I think some of the materials were brushed through and I had a hard time tackling exam and homework problems. I think it could be better if there was more coding elements and perhaps more exercises in class to better tackle assignments. But flexible assignment deadline was a plus as the professor was very accommodating.
Basics of machine learning from a statistical point of view - unsupervised learning, supervised learning, and some knowledge of neutral networks.
I learnt general ML- both the practical parts and theoretical/derivation.