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… https://coursecatalogue.maastrichtuniversity.nl/m.winands https://coursecatalogue.maastrichtuniversity.nl/cameron.browne Artificial Intelligence 8 Recommended reading Pattern Recognition and Machine Learning - C.M. Bishop; Bayesian Reasoning and Machine Learning - D. Barber; Gaussian Processes for Machine Learning - C.E. Rasmussen & C. Williams; The Elements of Statistical Learning - T. Hastie et al. KEN4154 Period 2 31 Oct 2022 23 Dec 2022 Print course description … There are often constraints on the order in which tasks, or ‘jobs’ can be performed, and there are usually capacity constraints of the machines. This leads to natural, industrially critical optimization problems. For example, a company might choose to buy many machines to process jobs, but then there is a risk that the machines will be underused, which is economically inefficient. On the other hand, too few machines, or an inappropriate ordering of tasks, may lead to machines spending a significant … sentiment- & emotion mining or clinical research. Prerequisites None. Recommended reading Introduction to Information Retrieval. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. Cambridge University Press, 2008. In bookstore and online: http://informationretrieval.org and Feldman, R., and Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press. KEN4153 Period 5 10 Apr 2023 9 Jun 2023 Print course description …

… to “stale news” which merely repeat previous revelations (Tetlock, 2011), and to focus on “attention- grabbing” stocks in the media rather than considering all available information (Barber and Odean, 2008). More generally, sentiments in news and online searches predict stock returns and trading volumes (Tetlock, 2007), stocks with low coverage have higher returns (Fang and Peress, 2009). Beyond traditional news media, activity in specialist chat rooms (e.g. RagingBull) predicts high volatility … (Antweiler and Frank, 2004), and sentiment indicators extracted from online forums and searches can predict returns (Chen et al., 2014). A growing economic literature also compares online and offline news (Gentzkow, 2011). Open questions remain in this field regarding social media: How is social media content processed? Is it processed differently from traditional online and offline news? Which models best describe the role of information from different sources? This project relies on … W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294. Barber, B. M., & Odean, T. (2007). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785-818. Chen, H., De, P., Hu, Y., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of …

… 36 5.3.5 Transparency in the VCM...................................................................................................... 37 5.3.6 Claims of Buyers ................................................................................................................... 38 5.4 Summary of Results … and deductive coding. 4.2.2 Reflection on Data Gathering For the case study's content analysis, most of the data collected stemmed from websites and were not third-party reviewed, introducing a potential bias into the thesis. This reliance on online sources, while necessary for capturing the most current information on the Acorn program, means that the quality and reliability of the data might vary. Efforts were made to select credible sources, but the variability in online content must be … GHG or not GHG: Accounting for diverse mitigation contributions in the post-2020 climate framework. OECD/IEA Climate Change Expert Group Papers, Article 2014/2. https://ideas.repec.org//p/oec/envaab/2014-2-en.html IAD Framework. (n.d.). Ostrom Workshop. Retrieved April 1, 2024, from https://ostromworkshop.indiana.edu/courses-teaching/teaching-tools/iad- framework/index.html https://www.theguardian.com/environment/2023/jan/18/revealed-forest-carbon-offsets-biggest-provider-worthless-verra-aoe …

… regions 14 3. Methodology The methodology used for this research is an exploratory mapping study, employing a mix of qualitative methods, namely extended desk research and interviews with experts and service providers. As a first step, a systematic online search was conducted to identify services for newcomers and other organisations and institutions working with newcomers. The online search covered each of the EMR’s sub-regions separately and used various search terms including “migrant … is the most international university of the Netherlands, with approximately half of the students population and one third of employees coming from abroad. https://www.willkommen-in-aachen.de/ 19 The Holland Expat Centre South (HECS)23 is a one-stop-shop for newly arrived internationals in the Southern parts of the Netherlands with a site in Maastricht. The HECS provides information and support for internationals and their families to help them settle into their new living and working environment. … and combined with guided tours or arts performances so that internationals also get to know Maastricht’s different neighbourhoods and cultural offers. In addition, HECS organises information workshops, e.g. for internationals who would like to buy a house in the Netherlands or start their own business. For these events, HECS cooperates with other specialised institutions in the field like the Starters Centrum Limburg24, the Housing Help Desk25 and the Maastricht Business Contact Centre26. On …

… to “stale news” which merely repeat previous revelations (Tetlock, 2011), and to focus on “attention- grabbing” stocks in the media rather than considering all available information (Barber and Odean, 2008). More generally, sentiments in news and online searches predict stock returns and trading volumes (Tetlock, 2007), stocks with low coverage have higher returns (Fang and Peress, 2009). Beyond traditional news media, activity in specialist chat rooms (e.g. RagingBull) predicts high volatility … (Antweiler and Frank, 2004), and sentiment indicators extracted from online forums and searches can predict returns (Chen et al., 2014). A growing economic literature also compares online and offline news (Gentzkow, 2011). Open questions remain in this field regarding social media: How is social media content processed? Is it processed differently from traditional online and offline news? Which models best describe the role of information from different sources? This project relies on … W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294. Barber, B. M., & Odean, T. (2007). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785-818. Chen, H., De, P., Hu, Y., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of …