Artificial intelligence and machine learning
Order ID |
53563633773 |
Type |
Essay |
Writer Level |
Masters |
Style |
APA |
Sources/References |
4 |
Perfect Number of Pages to Order |
5-10 Pages |
Description/Paper Instructions
Artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are closely related fields that involve the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects in images, and making decisions.
AI is a broad field that encompasses many different techniques and approaches, including rule-based systems, expert systems, and neural networks. Machine learning, on the other hand, is a subset of AI that is focused on the development of algorithms and models that can learn from data and improve their performance over time.
One of the key differences between AI and ML is that AI systems are designed to perform specific tasks, while ML systems are designed to improve their performance by learning from data. For example, a rule-based AI system might be designed to play chess, while a machine learning system might be designed to learn how to play chess by analyzing the moves of thousands of different games.
There are many different types of machine learning algorithms, each with their own strengths and weaknesses. Some of the most commonly used types include supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning algorithms are used to train models to perform specific tasks, such as classifying images or predicting the stock market. These algorithms are trained on a labeled dataset, where the input data is paired with the desired output. The algorithm learns to make predictions based on the input data by finding patterns in the data that are associated with the desired output.
Unsupervised learning algorithms are used to discover patterns and structure in unlabeled data. These algorithms are often used to identify clusters of similar data points or to reduce the dimensionality of the data.
Reinforcement learning algorithms are used to train agents to make decisions and take actions in an environment. These algorithms learn by receiving feedback in the form of rewards or penalties for their actions.
Deep learning is a subset of machine learning that is based on neural networks. Neural networks are inspired by the structure and function of the human brain and are made up of layers of interconnected nodes or “neurons.” These networks can be trained to perform a wide variety of tasks, such as image recognition, natural language processing, and game playing.
One of the key advantages of deep learning is that it can handle large amounts of data and can learn to extract features from the data that are relevant to the task at hand. This has led to significant breakthroughs in areas such as computer vision and natural language processing.
There are many different applications of AI and ML, including self-driving cars, speech recognition, image recognition, and natural language processing. These technologies are being used in a wide variety of industries, including healthcare, finance, and retail.
In conclusion, AI and ML are closely related fields that are focused on the development of computer systems that can perform tasks that typically require human intelligence. AI is a broad field that encompasses many different techniques and approaches, while ML is focused on the development of algorithms and models that can learn from data and improve their performance over time. Deep learning, a subset of machine learning that is based on neural networks, has also been proven to be effective in handling large amounts of data and extract features from data. AI and ML are being used in a wide variety of industries, including healthcare, finance, and retail, and have great potential to change our lives in the future.
RUBRIC
QUALITY OF RESPONSE |
NO RESPONSE |
POOR / UNSATISFACTORY |
SATISFACTORY |
GOOD |
EXCELLENT |
Content (worth a maximum of 50% of the total points) |
Zero points: Student failed to submit the final paper. |
20 points out of 50: The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. |
30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. |
40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. |
50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. |
Use of Sources (worth a maximum of 20% of the total points). |
Zero points: Student failed to include citations and/or references. Or the student failed to submit a final paper. |
5 out 20 points: Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. |
10 out 20 points: References to scholarly sources are occasionally given; many statements seem unsubstantiated. Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. |
15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. |
20 points: Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented. APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. |
Grammar (worth maximum of 20% of total points) |
Zero points: Student failed to submit the final paper. |
5 points out of 20: The paper does not communicate ideas/points clearly due to inappropriate use of terminology and vague language; thoughts and sentences are disjointed or incomprehensible; organization lacking; and/or numerous grammatical, spelling/punctuation errors |
10 points out 20: The paper is often unclear and difficult to follow due to some inappropriate terminology and/or vague language; ideas may be fragmented, wandering and/or repetitive; poor organization; and/or some grammatical, spelling, punctuation errors |
15 points out of 20: The paper is mostly clear as a result of appropriate use of terminology and minimal vagueness; no tangents and no repetition; fairly good organization; almost perfect grammar, spelling, punctuation, and word usage. |
20 points: The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. |
Structure of the Paper (worth 10% of total points) |
Zero points: Student failed to submit the final paper. |
3 points out of 10: Student needs to develop better formatting skills. The paper omits significant structural elements required for and APA 6th edition paper. Formatting of the paper has major flaws. The paper does not conform to APA 6th edition requirements whatsoever. |
5 points out of 10: Appearance of final paper demonstrates the student’s limited ability to format the paper. There are significant errors in formatting and/or the total omission of major components of an APA 6th edition paper. They can include the omission of the cover page, abstract, and page numbers. Additionally the page has major formatting issues with spacing or paragraph formation. Font size might not conform to size requirements. The student also significantly writes too large or too short of and paper |
7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. |
10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper. |
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Artificial intelligence and machine learning
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