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Course# ME-390: Foundations of artificial intelligence

Summary

This course provides the students with basic theory to understand the machine learning approach, and the tools to use the approach for problems arising in engineering applications.

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Related concepts (632)

Lectures in this course (22)

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Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

C++

C++ ('si:_plVs_plVs, pronounced "C plus plus" and sometimes abbreviated as CPP) is a high-level, general-purpose programming language created by Danish computer scientist Bjarne Stroustrup. First released in 1985 as an extension of the C programming language, it has since expanded significantly over time; modern C++ currently has object-oriented, generic, and functional features, in addition to facilities for low-level memory manipulation.

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Optimization of Paper PlanesME-390: Foundations of artificial intelligence

Explores the optimization of paper planes and soft structures for thrust generation.

Deep Learning FundamentalsME-390: Foundations of artificial intelligence

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Reinforcement Learning ConceptsME-390: Foundations of artificial intelligence

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Logistic Regression: Probabilistic InterpretationME-390: Foundations of artificial intelligence

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Unsupervised Learning: PCA & K-meansME-390: Foundations of artificial intelligence

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.