Daftar Isi
1. Machine Learning 1: What is Machine Learning
In this video, we motivate machine learning through shortcomings of classic algorithms design. We then briefly introduce the three ...
2. Machine Learning 46: Autoencoders
We introduce autoencoders for dimensionality reduction by passing a feature vector through a neural network with a bottleneck ...
3. Locality Sensitive Hashing 1: Nearest Neighbor Search
We introduce approximate Nearest Neighbor Search and motivate Locality Sensitive Hashing with a running example of ...
4. Machine Learning 7: Linear Regression, Motivation and Definition
We introduce linear regression for solving regression problems in supervised learning. This video motivates the model and ...
5. Lower Bounds for Dynamic Data Structures II
Kasper
6. Artificial Intelligence and Machine Learning: How does it work
In this video, I explain the fundamental ideas and mathematics underlying artificial intelligence (AI). We start with very simple ...
7. Machine Learning 17: Overfitting
We discuss overfitting and reasons for why it occurs. Concretely, we demonstrate how noise in the training data causes powerful ...
8. TCS+ Talk: Kasper Green Larsen (Aarhus University)
Title: Bagging is an Optimal PAC Learner Abstract: Determining the optimal sample complexity of PAC learning in the realizable ...
9. Machine Learning 44: Principal Component Analysis - Minimizing Loss in Projection
We derive the Principal Component Analysis (PCA) algorithm via a different approach, namely by thinking of it as minimizing the ...
10. Machine Learning 45: Principal Component Analysis - Practical Considerations
We conclude our study of Principal Component Analysis (PCA) by discussing issues of scaling of input features. We argue why ...
11. Time/Space Tradeoffs for Generic Attacks on Delay Functions
Delay functions have the goal of being inherently slow to compute. They have been shown to be useful for generating public ...
12. Machine Learning 3: Decision Trees, Part 1
We introduce decision trees as an alternative to linear models and discuss issues regarding the training of such models.
13. From Theoretical Computer Science to Learning Theory
While machine learning theory and theoretical computer science are both based on a solid mathematical foundation, the two ...
14. Kasper Green Larsen - Tutorial (Part 1) - Multiphase & OMV Conjectures & Implications to Dynamic LBs
This is PART 1 of Kasper
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