Review of Course/Syllabus Introduction to Machine Learning Part 1 ‹#› Learning Objectives for This Class Distinguish between learning and non-learning in AI Know when to apply neural nets Comfortable with “genetic algorithm” ‹#› By covering the basics of various techniques, we will compare how they can be applied. We will restrict this to two major […]
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1. Introduction and JUnit Rule-based Systems 1 Despite their success, technologies like neural nets and SVM’s are knowledge free—”dumb” in many ways. To illustrate this, suppose that your child answered “105” when asked “how much is 98 + 9?” But when you ask “why”, he answers … because I just happen to know that 97
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APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Week 3: • End-to-endMachineLearning • Data Retrieval and Preparation • Plotting and Visualization • MakingPredictions • Decision Trees Prof. Agenda ➢Today’s focus is on Foundations of Learning 1. End-to-end machine learning 2. Python Libraries —NumPy —Matlplotlib —Pandas —Scikit-Learn 3. Decision Trees 2 Part 1 End-to-End
APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Lecture 1: • Introduction • CourseOverview • Machine Learning Overview • K-nearestNeighbourClassifier Prof. 1 2 Instruction Team Instructor: Prof. Head-TA: Zadeh TA: TA: Haoyan (Max) A: TA: Get to know the instruction team: https://q.utoronto.ca/courses/223861/pages/course-contacts 3 Communication ➢Preferred contact method for a quick response: Piazza; 1.
APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Week 5: • LinearAlgebra • Analytical Geometry • Data Augmentation Prof. Mid-term assessment logistics ➢Platform: Crowdmark ➢ is available now (emailed to you) to get familiar with the logistics. It will not be graded. Due date 8 Oct at 21:00. ➢Some practice problems and solutions
1. Introduction and JUnit Module 1 Part 1 of 2 Introduction and Agents 1 1 Class Learning Objectives Understand objectives of AI Apply agents 2 The word “AI” has become ubiquitous. But what, exactly, does it mean? This part answers that question. A good portion of AI approaches problems from the perspective of agents—autonomous objects
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Cloud Computing INFS3208 Re-cap 1. CourseOrientation 2. HistoryandDefinitionofCloudComputing 3. BusinessDriversforcreationofCloudComputing – Capacity Planning, Cost Reduction, Organisational Agility 4. TechnologiesthatimpactCloudComputing – Clustering, Grid Computing, Virtualisation 5. CloudCharacteristics – On-demand usage, Ubiquitous access, Multitenancy, Elastic, Measurable, Resilient. CRICOS code 00025B 2 Outline • CloudDeliveryModels • CloudDeployModels • Cloud-EnablingTechnologies – Broadband Networks and Internet Architecture – Virtualisation Technology
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Computer Science is the science of delegation Key question: How do we delegate tasks to a machine? Hardware engineering: How to design machines able to effectively & efficiently undertake the tasks we delegate to them? Programming theory: How do we best write instructions for machines to do what we what we want them
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AI and Ethics Outline This week we talk about AI and Ethics Why is this topic important Regulatory and legal constraints Ethical dimensions Fairness (non-bias), transparency, explanation, rectification Features of this domain Deciding what we ourselves should do in specific situations. 2 Why is it important to consider ethics?
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Lecture 11 Lectures 12-13 Copyright By PowCoder代写 加微信 powcoder Ontologies & Machine Learning Review & Final Exam The contents are mainly taken from “A Semantic Web Primer – MIT press” The slides are prepared by Dr. A Semantic Web Primer Ontologies and Machine Learning Ontologies for Machine Learning Machine learning for Ontology Development A Semantic