3.3 Assessment Record for Written Assessment (Q&A)
Start Time:
Candidate's Name :
Assessor's Name :
End Time :
Learning Unit: Predictive Modelling with Pytorch
Written Assessment (Q&A)
Enabling Learning Outcomes
Abilities and Knowledge
Tick c NYC
Evidence of "C" and "NYC must be recorded
ELO1a - Learners will be able to apply K1: Organisational domain(s) and key business machine learning principles to gain processes business insights. K6: Methods to build a data model ELO1b - Learners will be able to K8: Methods to interpret patterns in data and their aggregate data to help test problem relevance to business issues using Pytorch ELO2 - Learners will be able to apply K7: Methods to use data mining to discover new predictive data modeling techniques to business insights identify underlying trend and patterns K11: Use of statistical techniques, experimental in data using neural networks. techniques and hypothesis testing ELO3 - Learners will be able to K5: Methods to develop prototype algorithms develop prototype classification model K9: Range of established and novel tools and using machine learning techniques to techniques used in developing new business insights gain new insight from data. ELO4 - Learners will be able to identify K2: Methods to use analytics to tell the story of the data patterns using convolutional neural K4: Methods to identify and prioritise the problems to be network model to derive insights and solved make decision. ELO5 - Learners will be able to use K3: Methods to use exploratory visual analysis and Tensorboard data visualisation tool to predictive modelling create interactive visualizations of data K10 Methods to apply complex software tools to analyse data C: Competent; NYC: Not Yet Competent
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3.4 Assessment Record for Written Assessment (PP)
Start Time :
Candidate's Name :
Assessor's Name :
End Time :
Learning Unit: Predictive Modelling with Pytorch
Written Assessment (PP)
Enabling Learning Outcomes
Abilities and Knowledge
Tick c NYC
Evidence of "C" and "NYC" must be recorded
ELO1a - Learners will be able to A5: Assemble data aggregations to build data models to apply machine learning principles to help test problem hypotheses gain business insights. A8: Assess the business insights presented to determine ELO1b - Learners will be able to impact of insights on organisation aggregate data to help test problem using Pytorch ELO2 - Learners will be able to apply A1: Apply predictive data modelling techniques to identify predictive data modeling techniques underlying trends and patterns in data using statistical to identify underlying trend and computing tools, methods and procedures patterns in data using neu