This presentation discusses the selection sort algorithm. Before writing code students should be able to sort an array on paper and show how the array is reorganized after each iteration of the selection sort algorithm. See my web link below. – – – – – – – – – – – – – – – –
From playlist Java Programming
Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1
The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit
From playlist Introduction to Machine Learning 101
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
(ML 12.1) Model selection - introduction and examples
Introduction to the basic idea of model selection, and some examples: linear regression using MLE, Bayesian linear regression, and k-nearest neighbor.
From playlist Machine Learning
Scheduling: The List Processing Algorithm Part 1
This lesson explains and provides an example of the list processing algorithm to make a schedule given a priority list. Site: http://mathispower4u.com
From playlist Scheduling
DeepMind x UCL RL Lecture Series - Model-free Control [6/13]
Research Scientist Hado van Hasselt covers prediction algorithms for policy improvement, leading to algorithms that can learn good behaviour policies from sampled experience. Slides: https://dpmd.ai/modelfreecontrol Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Reinforcement Learning 4: Model-Free Prediction and Control
Hado van Hasselt, Research Scientist, discusses model-free prediction and controls as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
DeepMind x UCL RL Lecture Series - Multi-step & Off Policy [11/13]
Research Scientist Hado van Hasselt discusses multi-step and off policy algorithms, including various techniques for variance reduction. Slides: https://dpmd.ai/offpolicy Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Introduction (3): Supervised Learning
Basics of supervised learning; regression, classification
From playlist cs273a
Lecture15. Cascades in networks. Influence maximization
Network Science 2021 @ HSE
From playlist Network Science, 2021
Spectral properties of steplength selections in gradient (...) - Zanni - Workshop 1 - CEB T1 2019
Zanni (Univ. Modena) / 08.02.2019 Spectral properties of steplength selections in gradient methods: from unconstrained to constrained optimization The steplength selection strategies have a remarkable effect on the efficiency of gradient-based methods for both unconstrained and constrai
From playlist 2019 - T1 - The Mathematics of Imaging
Chao Gao: Statistical Optimality and Algorithms for Top-K Ranking - Lecture 1
CIRM VIRTUAL CONFERENCE The second presentation will be focused on total ranking. The problem is to find a permutation vector to rank the entire set of players. We will show that the minimax rate of the problem with respect to the Kendall’s tau loss exhibits a transition between an expon
From playlist Virtual Conference
Challenges in developing learning algorithms to personalize treatment in real time
Distinguished Visitor Lecture Series Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time Susan A Murphy Harvard University, USA
From playlist Distinguished Visitors Lecture Series
8 2 Randomized Selection Analysis 21 min
From playlist Algorithms 1
Paul-André Melliès - A gentle introduction to template games and linear logic
Game semantics is the art of interpreting formulas (or types) as games and proofs (or programs) as strategies. In order to reflect the interactive behaviour of pro- grams, strategies are required to follow specific scheduling policies. Typically, in the case of a sequential programming lan
From playlist Combinatorics and Arithmetic for Physics: Special Days 2022
9.x: Genetic Algorithms and Evolutionary Computing - The Nature of Code
This video covers genetic algorithms and looks at how they are applied in 3 scenarios. 1: search problems where brute force is an impossibility (infinite monkey theorem). 2: physics-based systems 3: Interactive selection (i.e. user behavior driven fitness). This video is excerpted
From playlist The Nature of Code: Simulating Natural Systems
(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
Stanford Seminar - Exposure to Political Diversity Online
Sean Munson University of Washington This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will di
From playlist Stanford Seminars