/ai-and-machine-learning

Topics in artificial intelligence and machine learning related statistical machine learning, deep learning, supervised and unsupervised learning, knowledge representation and reasoning

AI and Machine Learning

Topics in artificial intelligence and machine learning related statistical machine learning, deep learning, supervised and unsupervised learning, knowledge representation and reasoning. Topics extracted from ASU MasterTrack. During the preparation of this work the author(s) used LLMs outputs in order to elaborate about the topics and retreive references. These references were verified and information validated. After using this tool/service, the author(s) reviewed and edited the content.

Statistical Machine Learning

  • Distinguish between supervised learning and unsupervised learning

  • Apply common probability distributions in machine learning applications

  • Use cross validation to select parameters

  • Use maximum likelihood estimate (MLE) for parameter estimation

  • Implement fundamental learning algorithms such as logistic regression and k-means clustering

  • Implement more advanced learning algorithms such as support vector machines and convolutional neural networks

  • Design a deep network using an exemplar application to solve a specific problem

  • Apply key techniques employed in building deep learning architectures

Artificial Intelligence

  • Apply logical reasoning and programming to produce solutions for real-world problems

  • Use probabilistic inference to navigate uncertain information efficiently

  • Determine appropriate machine learning methods for a given scenario or dataset

  • Evaluate the challenges in perception systems for AI

  • Utilize sensors to execute perception tasks and their applications in intelligent systems

  • Apply algorithms to train an image classifier

  • Design an agent that can plan and act to achieve given objectives using noisy sensors and actuators

Knowledge Representation and Reasoning

  • Discuss the foundations of KRR

  • Explain different categories of representation and reasoning tasks

  • Assess the tradeoff between representation and reasoning

  • Identify which knowledge-based techniques are appropriate for which task

  • Apply KRR systems to challenging real-world problems

Intro to Deep Learning in Visual Computing

  • Gain an understanding of the mathematics (Statistics, Probability, Calculus, Linear Algebra and optimization) needed for designing machine learning algorithms

  • Learn how machine learning models fit data and how to handle small and large datasets

  • Understand the workings of different components of deep neural networks

  • Design deep neural networks for real-world applications in computer vision

  • Learn to transfer knowledge across neural networks to develop models for applications with limited data

  • Get introduced to deep learning approaches for unsupervised learning