This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch.
This week’s module introduces the field of statistical learning, exploring its scope and practical applications across various domains. Students will analyze how statistical learning techniques are used to make predictions, infer relationships, and uncover patterns in complex datasets. The module a…
This week’s module introduces you to the concept of Maximum Likelihood Estimation (MLE) and its application in statistical modeling. Through this material, you will gain a thorough understanding of how to mathematically implement MLE and apply it to real-world datasets. First, we will revisit found…
In this module, you will gain a comprehensive understanding of supervised machine learning, from model training to evaluation. Specifically, you will interpret each step in the learning process and apply training and evaluation techniques to real-world data. This will enable you to fit and assess m…
This week, we will focus on the foundational principles of linear regression, a key technique in predictive modeling. You will learn to apply linear regression models and derive the ordinary least squares (OLS) formulation, gaining insight into how OLS is used to fit data accurately. We will also c…
This week, we will dive into advanced techniques for linear regression, with a focus on regularization. You will have the opportunity to explore the concepts of Lasso and Ridge regression and learn how to formulate and apply these regularization methods to linear models. The module also covers poly…
This week’s module offers a comprehensive introduction to logistic regression, a fundamental technique in classification tasks. You will learn to apply logistic regression to binary and multi-class classification problems, starting with the derivation of the maximum likelihood formulation specific…
This week, we introduce Support Vector Machines (SVMs) as a powerful tool for discriminative classification. You will start by understanding the mathematical formulation of SVMs, focusing on margin optimization to maximize model separation between classes. We then delve into various kernel function…
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Northeastern University is in Tier 3: good universities, respected companies, nonprofits and well-known teachers of our institution ranking (74/100).
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
Stanford CS25: Transformers United Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs)…
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural n…
