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DTSTART;VALUE=DATE:20361022
DTEND;VALUE=DATE:20361025
DTSTAMP:20260405T005520
CREATED:20250802T123021Z
LAST-MODIFIED:20250828T151340Z
UID:10000501-2108246400-2108505599@prstats.org
SUMMARY:Introduction to Machine Learning
DESCRIPTION:Machine Vision using Python\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, November 18th\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		MVUPPR RECORDED\n	\n	MVUPPR RECORDED\n\n	\n		\n		\n				\n					£\n					450.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for MVUPPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for MVUPPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 18th\n				Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n			\n				\n				\n				\n				\n				Wednesday 19th\n				Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n			\n				\n				\n				\n				\n				Thursday 20th\n				Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage
URL:https://prstats.org/course/introduction-to-machine-learning-imlrpr/
LOCATION:Recorded\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/NWAE01-2.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20361024
DTEND;VALUE=DATE:20361027
DTSTAMP:20260405T005520
CREATED:20250904T125855Z
LAST-MODIFIED:20250904T125917Z
UID:10000530-2108419200-2108678399@prstats.org
SUMMARY:Machine Learning Intermediate to Advanced
DESCRIPTION:Machine Vision using Python\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, November 18th\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		MVUPPR RECORDED\n	\n	MVUPPR RECORDED\n\n	\n		\n		\n				\n					£\n					450.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for MVUPPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for MVUPPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 18th\n				Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n			\n				\n				\n				\n				\n				Wednesday 19th\n				Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n			\n				\n				\n				\n				\n				Thursday 20th\n				Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage
URL:https://prstats.org/course/machine-learning-intermediate-to-advanced-mliapr/
LOCATION:Recorded\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MLIAPR.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20361025
DTEND;VALUE=DATE:20361028
DTSTAMP:20260405T005520
CREATED:20250904T130617Z
LAST-MODIFIED:20251120T180554Z
UID:10000527-2108505600-2108764799@prstats.org
SUMMARY:Machine Learning using Python
DESCRIPTION:Machine Vision using Python\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, November 18th\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		MVUPPR RECORDED\n	\n	MVUPPR RECORDED\n\n	\n		\n		\n				\n					£\n					450.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for MVUPPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for MVUPPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 18th\n				Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n			\n				\n				\n				\n				\n				Wednesday 19th\n				Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n			\n				\n				\n				\n				\n				Thursday 20th\n				Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage
URL:https://prstats.org/course/machine-learning-using-python-mluppr/
LOCATION:Recorded\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MLUPPR.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20361029
DTEND;VALUE=DATE:20361101
DTSTAMP:20260405T005520
CREATED:20250904T130605Z
LAST-MODIFIED:20250904T133615Z
UID:10000529-2108851200-2109110399@prstats.org
SUMMARY:Machine Vision using Python
DESCRIPTION:Machine Vision using Python\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, November 18th\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	\n		MVUPPR RECORDED\n	\n	MVUPPR RECORDED\n\n	\n		\n		\n				\n					£\n					450.00\n				\n						\n\n			\n			Unlimited	\n				\n			\n				Open the ticket description.\n				More			\n			\n				Close the ticket description.\n				Less			\n	\n	\n\n			\n			\n	Decrease ticket quantity for MVUPPR RECORDED\n	-\n		\n	\n		Quantity	\n	\n\n		\n	Increase ticket quantity for MVUPPR RECORDED\n	+\n		\n	\n				\n		\n\n		\n	\n		Quantity:	\n	0\n\n	\n	\n		Total:	\n	\n		\n				\n					£\n					0.00\n				\n				\n\n			\n	Get Tickets\n	\n\n	\n		\n	\n\n		\n	\n\n		\n	\n\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 18th\n				Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n			\n				\n				\n				\n				\n				Wednesday 19th\n				Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n			\n				\n				\n				\n				\n				Thursday 20th\n				Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage
URL:https://prstats.org/course/machine-vision-using-python-mvuppr/
LOCATION:Recorded\, United Kingdom
CATEGORIES:General Recorded Courses,Previously Recorded Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MVUPPR-1.jpg
GEO:55.378051;-3.435973
END:VEVENT
END:VCALENDAR