{"text_format":"1","workbook":"xlsx","n_sheets":1,"sheets":["MCQs Template"],"not_rendered":{"charts":0,"images":0,"macros":1,"external_links":0,"defined_names":0,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900"}

{"sheet":"MCQs Template","index":0,"n_rows":964,"n_cols":5,"rows_kept":964,"cut":false,"hidden":false,"merged":[]}
[]
["CVE.U1.E1.PC1",null,"The student understands the differences between data, information, knowledge, insight and wisdom."]
[]
["Number","Correct?","Question"]
[]
[1,"B","In the DIKW hierarchy, information represents the level with most value.","A","Yes"]
[null,null,null,"B","No"]
[]
[2,"B","The extraction of patterns from information leads to wisdom.","A","Yes"]
[null,null,null,"B","No"]
[]
[3,"A","Data are raw and are facts that alone are not meaningful.","A","Yes"]
[null,null,null,"B","No"]
[]
[4,"B","Wisdom is the top level of the DIKW hierarchy and answers the \"How?\" question.","A","Yes"]
[null,null,null,"B","No"]
[]
[" CVE.U1.E1.PC2",null,"The student is able to define artificial intelligence."]
[]
["Number","Correct?","Question"]
[]
[1,"B,C","What is artificial intelligence?","A","Artificial Intelligence describes the large volume of data – both structured and unstructured – that inundates a business on a daily basis."]
[null,null,null,"B","Artificial Intelligence is a branch of Computer Science."]
[null,null,null,"C","Artificial Intelligence seeks to simulate the human process of learning and decision making."]
[null,null,null,"D","Artificial Intelligence is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it."]
[]
["CVE.U1.E1.PC3",null,"The student understands the need, purpose and impacts of artificial intelligence. "]
[]
["Number","Correct?","Question"]
[]
[1,"A","Artificial Intelligence impacts almost every domain of our daily lives, smart home devices and surgical robots are examples of AI applications.","A","Yes"]
[null,null,null,"B","No"]
[]
[2,"A","Machines are not affected by emotions or human problems such as lack of sleep or hunger​, allowing the reduction of errors and  avoiding performance fluctuations.","A","Yes"]
[null,null,null,"B","No"]
[]
[3,"B","Artificial Intelligence is associated to low production and repair costs.​","A","Yes"]
[null,null,null,"B","No"]
[]
["CVE.U1.E2.PC1",null,"Understand the connection between artificial intelligence and machine learning."]
[]
["Number","Correct?","Question"]
[]
[1,"C","What is the connection between Artificial Intelligence and Machine Learning?","A","Artificial Intelligence and Machine Learning are branches of Statistics."]
[null,null,null,"B","Artificial Intelligence and Machine Learning are separate fields."]
[null,null,null,"C","Machine Learning is a current application of Artificial Intelligence."]
[null,null,null,"D","Artificial Intelligence is a subset of Machine Learning."]
[]
[2,"B","Machine Learning is ","A","a subset of Artificial Intelligence and Deep Learning."]
[null,null,null,"B","a subset of Computer Science and Artificial Intelligence."]
[null,null,null,"C","a subset of Data Mining and Artificial Intelligence."]
[]
["CVE.U1.E2.PC2",null,"Define and characterize machine learning."]
[]
["Number","Correct?","Question"]
[]
[1,"B,C","What is machine learning? ","A","Machine Learning is a method of storing and transmitting data."]
[null,null,null,"B","Machine Learning uses statistical methods to enable machines to learn and improve with experience.​"]
[null,null,null,"C","Machine Learning enables computers to learn automatically without human intervention."]
[]
["CVE.U1.E2.PC3",null,"List and explain the different types of machine learning methods."]
[]
["Number","Correct?","Question"]
[]
[1,"D","Machine Learning algorithms can be grouped into the following categories","A","Supervised and Unsupervised Learning."]
[null,null,null,"B","Supervised, Unsupervised and Semi-supervised Learning."]
[null,null,null,"C","Supervised, Unsupervised and Reinforcement Learning."]
[null,null,null,"D","Supervised, Unsupervised, Semi-supervised and Reinforcement Learning."]
[]
[2,"C,D","What is supervised learning?","A","Supervised learning trains algorithms based on unlabeled datasets."]
[null,null,null,"B","In supervised learning, there isn't a relationship between the input and the output."]
[null,null,null,"C","In supervised learning, there is a relationship between the input and the output."]
[null,null,null,"D","Supervised learning trains algorithms based on labeled datasets."]
[]
[3,"C","The following are supervided learning algorithms","A","Logistic Regression, Fuzzy C-means and Decision Trees."]
[null,null,null,"B","K-means, Random Forest and Support Vector Machines."]
[null,null,null,"C","Naïve Bayes, Decision Trees and Multiple Linear Regression."]
[]
[4,"A,C","In classification","A","the output is one or more discrete labels."]
[null,null,null,"B","the output is a continuous quantity."]
[null,null,null,"C","the nature of the predicted data is unordered."]
[null,null,null,"D","the nature of the predicted data is ordered.​"]
[]
[5,"A","Support Vector Machines are","A","classification algorithms."]
[null,null,null,"B","regression algorithms."]
[null,null,null,"C","probability estimation algorithms."]
[]
[6,"A","Logistic Regression are","A","classification algorithms."]
[null,null,null,"B","regression algorithms."]
[null,null,null,"C","probability estimation algorithms."]
[]
[7,"C","What are the domain applications of supervised learning?","A","Fraud detection and gaming are examples of supervised learning applications."]
[null,null,null,"B","Optimal character recognition and big data visualization are examples of supervised learning applications."]
[null,null,null,"C","Information retrieval, spam detection and speech recognition are examples of supervised learning applications."]
[]
[8,"A,B","What is unsupervised learning?","A","Unsupervised learning trains algorithms based on unlabeled datasets."]
[null,null,null,"B","In unsupervised learning, there isn't a relationship between the input and the output."]
[null,null,null,"C","In unsupervised learning, there is a relationship between the input and the output."]
[null,null,null,"D","Unsupervised learning trains algorithms based on labeled datasets."]
[]
[9,"C","The following are unsupervided learning algorithms","A","Logistic Regression, Multiple Linear Regression and Decision Trees."]
[null,null,null,"B","Random Forest, K-means and Support Vector Machines."]
[null,null,null,"C","K-means, Fuzzy C-means and Gaussian models."]
[]
[10,"B","What are the domain applications of unsupervised learning?","A","Fraud detection and gaming are examples of supervised learning applications."]
[null,null,null,"B","Recommender systems and big data visualization are examples of supervised learning applications."]
[null,null,null,"C","Information retrieval, spam detection and speech recognition are examples of supervised learning applications."]
[]
[11,"C","What kind of data is used in semi-supervised learning?","A","In semi-supervised learning, the dataset consists of unlabelled data."]
[null,null,null,"B","In semi-supervised learning, the dataset consists of labelled data."]
[null,null,null,"C","In semi-supervised learning, the dataset consists of a combination of labelled and unlabelled data."]
[]
[12,"A","Semi-supervised learning","A","can be either transductive or inductive learning."]
[null,null,null,"B","is a particular case of unsupervised approach."]
[null,null,null,"C","groups the data using supervised learning algorithms and then, unsupervised learning algorithms are applied."]
[]
[13,"B","The following are semi-supervided learning algorithms","A","Support Vector Machine, Graph-based models and Random Forest."]
[null,null,null,"B","Self-Training​, Mixture models and Graph-based models."]
[null,null,null,"C","Generative models​, K-means and Logistic Regression."]
[]
[14,"A,C","When should we use semi-supervised models?","A","When there is a large amount of unlabeled data in-domain."]
[null,null,null,"B","When there is a scarcity of unlabeled data in-domain."]
[null,null,null,"C","When labelled data is only available out-of-domain."]
[null,null,null,"D","When labelled data is only available in-domain."]
[]
[15,"B","What is reinforcement learning?","A","reinforcement learning is a type of dynamic programming that forms algorithms using labeled input/output pairs​."]
[null,null,null,"B","reinforcement learning is a type of dynamic programming that forms algorithms using a reward and punishment system​."]
[null,null,null,"C","reinforcement learning is a type of static programming that forms algorithms using a reward and punishment system​."]
[]
[16,"C","The following are examples of reinforcement learning algorithms","A","Model Based Value Expansion​​, Deep Deterministic Policy Gradients​​ and Support Vector Machines."]
[null,null,null,"B","Deep Deterministic Policy Gradients​​, Logistic Regression and Imagination-Augmented Agents​."]
[null,null,null,"C","AlphaZero, Model Based Value Expansion​​ and Imagination-Augmented Agents​."]
[]
[17,"B","Reinforcement learning algorithms can be categorized into","A","Policy optimization and model-based."]
[null,null,null,"B","Model-based and model-free."]
[null,null,null,"C","Policy optimization and Q-learning."]
[]
[18,"C","Model-free reinforcement learning","A","has two approaches, namely learning the model and learn by given the model."]
[null,null,null,"B","uses experience to develop an internal model of transitions and immediate results in the environment​."]
[null,null,null,"C","relies on trial-and-error experience to establish the optimal policy.​"]
[]
[19,"B","Model-based reinforcement learning","A","has two approaches, namely policy optimization and Q-learning."]
[null,null,null,"B","uses experience to develop an internal model of transitions and immediate results in the environment​."]
[null,null,null,"C","relies on trial-and-error experience to establish the optimal policy.​"]
[]
[20,"A","Model-free methods are statistically less efficient than model-based methods​.","A","Yes"]
[null,null,null,"B","No"]
[]
[21,"A","Robots can implement reinforcement learning algorithms to learn how to walk.​","A","Yes"]
[null,null,null,"B","No"]
[]
[22,"B","Reinforcement learning algorithms achieve short-term results.","A","Yes"]
[null,null,null,"B","No"]
[]
[23,"B","Reinforcement learning is preferable to use for solving simple problems.","A","Yes"]
[null,null,null,"B","No"]
[]
["CVE.U1.E3.PC1",null,"Define deep learning."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Deep Learning is a subset of machine learning​","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U1.E3.PC2",null,"The student understands the connection and the differences between artificial intelligence, machine learning and deep learning."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Deep Learning is a subset of machine learning​","A","True"]
[null,null,null,"B","False"]
[]
[]
[2,"B","Machine Learning is a component of deep learning process","A","True"]
[null,null,null,"B","False"]
[]
["Number","Correct?","Question"]
[]
[3,"B","Machine Learning pursues the optimal solution while Artificial Intelligence will only come up with a solution that is either optimal or not​","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U1.E3.PC3",null,"The student understands the nature, purpose and applications of an artificial neuron."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Neural networks are based on artificial neurons. They are able to:","A","Learn the relationships between inputs and outputs that are nonlinear and complex​. Although, they cannot model the relationships."]
[null,null,null,"B","Predict rare events, according to there relationships"]
[null,null,null,"C","Reveal hidden relationships even if they don't have patterns or relationships"]
[null,null,null,"D","Model data, if it is no volatile"]
[]
[2,"A,B,C","Artificial Neurons are highly applicable to the real world. Some examples of application are: ","A","Credit card fraud detection"]
[null,null,null,"B","Medical and disease diagnosis"]
[null,null,null,"C","Robotic control systems"]
[]
["CVE.U1.E3.PC4",null,"The student is able to recognize and understand the similarities between a computational neuron and a human neuron"]
[]
["Number","Correct?","Question"]
[]
[1,"A","Both human and computational neuron use eletrical signals to send messages","A","True"]
[null,null,null,"B","False"]
[]
[]
[2,"B","Human neuron and Computational neuron work at a similar speed","A","True"]
[null,null,null,"B","False"]
[]
[]
[3,"A","Machines are way less efficient than biological systems. ","A","True"]
[null,null,null,"B","False"]
["AIT.U1.E4.PC1",null,"Know the different application domains of artificial intelligence. ​"]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C,D","Artificial Intelligence can be applied to","A","Marketing and Social Media"]
[null,null,null,"B","Finances and Banking"]
[null,null,null,"C","Healthcare"]
[null,null,null,"D","Space Exploration"]
["AIT.U1.E4.PC2",null,"Analyse and discuss several examples and applications of artificial intelligence. ​"]
[]
["Number","Correct?","Question"]
[]
[1,"A","The following are examples of artificial intelligence applications","A","Smartphones, drones and autonomous vehicles."]
[null,null,null,"B","Smart home devices, thermostats and facial recognition."]
[]
["AIT.U1.E4.PC3",null,"Recognizes the challenges surrounding artificial intelligence approaches. ​"]
[]
["Number","Correct?","Question"]
[]
[1,"B,C","What are the challenges of artificial intelligence?","A","Statistics and analytics"]
[null,null,null,"B","Transparency and trustability"]
[null,null,null,"C","Legal and ethical issues"]
[null,null,null,"D","Big Data"]
[]
["CVE.U1.E5.PC1 ",null,"Recognize and critically understand the issues and ethical concerns surrounding artificial intelligence."]
[]
["Number","Correct?","Question"]
[]
[1,"B,C","Some artificial intelligence issues are.    ","A","Low Cost"]
[null,null,null,"B","Unemployment"]
[null,null,null,"C","No Emotions"]
[]
[2,"A,B","Some artificial intelligence ethical issues are.    ","A","Privacy and Data protection​"]
[null,null,null,"B","Transparency"]
[null,null,null,"C","Unsafely"]
[]
["CVE.U1.E5.PC2 ",null,"Think of ways to solve or diminish those issues and ethical concerns."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Modernizing legal frameworks will clarify the path to ethical AI development.​","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E1.PC1",null,"Define Computer Vision."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Computer Vision is a branch of computer science that specializes in developing digital systems that can process, analyze and make sense of images or videos in a similar way to human beings.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E1.PC2",null,"Understand the functioning of the human visual system"]
[]
["Number","Correct?","Question"]
[]
[1,"A","The focus and projection of the image is performed by.","A","the retina"]
[null,null,null,"B","the iris"]
[]
["CVE.U2.E1.PC3",null,"Understand the origin, need and purpose of computer vision"]
[]
["Number","Correct?","Question"]
[]
[1,"B","First Digital Image Scanner appears","A","in 2000"]
[null,null,null,"B","in 1959"]
[null,null,null,"C","in 1963"]
[]
[2,"A,B,C","Why we need computer vision? ","A","Safety"]
[null,null,null,"B","Health"]
[null,null,null,"C","Security"]
[]
["CVE.U2.E1.PC4",null,"Understand the digital imaging process."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Are Image Acquisition, Compression, and Object Recognition part of the digital image process?","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E1.PC5",null,"Understand  how computer vision fits into artificial intelligence"]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Computer vision is important in many different ways:​","A","Edge detection"]
[null,null,null,"B","Facial Recognition"]
[null,null,null,"C","Feature matching"]
[]
["CVE.U2.E1.PC6",null,"Identify different  application domains of computer vision."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C,D","What are the application domains of computer vision?","A","Retail"]
[null,null,null,"B","Manufacturing"]
[null,null,null,"C","Government"]
[null,null,null,"D","Healthcare"]
[]
["CVE.U2.E2.PC1",null,"Understand what CV techniques are and what are their purposes.​"]
[]
["Number","Correct?","Question"]
[]
[1,"A","Why object detection is important?","A","help us to understand and to analyze scenes in images or video."]
[null,null,null,"B","classifies all the pixels of an image into meaningful classes of objects."]
[null,null,null,"C","is used for detecting, analyzing, and interpreting images."]
[]
["CVE.U2.E2.PC2",null,"List and Understand the most used state-of-the-art vision techniques."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Which one is an example of a computer vision technique?","A","Object detection"]
[null,null,null,"B","Object tracking"]
[null,null,null,"C","3D objects"]
[null,null,null,"D","Digital Data"]
[]
[2,"A","Image classification is a technique used for detecting, analyzing, and interpreting images.​ It is a key part of computer vision.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E3.PC1",null,"Understands what an image is."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","What is an image?","A","Representation of something or someone or a photograph or an idea you're picturing in your head or the way you or others think of you."]
[null,null,null,"B","Physical likeness or representation of a person, animal, or thing, photographed, painted, sculptured, or otherwise made visible."]
[null,null,null,"C","The visual impression of something, produced variously as by reflection from a mirror, refraction through a lens, electromagnetic or ultrasound scanning, etc."]
[]
["CVE.U2.E3.PC2",null,"Know the different types of images.​"]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,D","Which one is an example of a type of image?","A","Black and white"]
[null,null,null,"B","Binary"]
[null,null,null,"C","12 bit color format"]
[null,null,null,"D","Color"]
[]
["CVE.U2.E3.PC3",null,"Arrange an image as a matrix."]
[]
["Number","Correct?","Question"]
[]
[1,"A","An image is an array, or a matrix, of square pixels arranged in columns and rows.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E3.PC4",null,"Define the process of image formation in the eye."]
[]
["Number","Correct?","Question"]
[]
[1,"C","How many steps the process of image formation in the eye have?","A",3]
[null,null,null,"B",4]
[null,null,null,"C",5]
[null,null,null,"D",6]
[]
["CVE.U2.E3.PC5",null,"Understand the process of digital image formation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","In a simple model, first the scene is illuminated by a single source, then the scene reflects radiation towards the camera and lastly, the camera senses it via chemicals on film.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E3.PC6",null,"Understand the steps behind image formation."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Sensor converts​ continuous signal to discrete signal​","A","True "]
[null,null,null,"B","False"]
[]
["CVE.U2.E3.PC7",null,"List, define and understand the factors in image formation: geometry, radiometry, photometry and digitization."]
[]
["Number","Correct?","Question"]
[]
[1,"C","Geometry describes the projection of:","A","three-dimensional​ (3D) world"]
[null,null,null,"B","two-dimensional​ (2D) image plane"]
[null,null,null,"C","dealing with geometric relations between the 3D world and its projection into 2D image."]
[]
[2,"B","Radiometry does not enables to explain image formation mechanism.","A","True"]
[null,null,null,"B","False"]
[]
[3,"A","Photometry is a concept that is concerned with ways of measuring the ​intensity of light.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E3.PC8",null,"Recognize and comprehend the challenges behind image formation."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Challenges behind image formation.","A","Complex algorithms"]
[null,null,null,"B","Perspective distortion"]
[null,null,null,"C","Huge amounts of data"]
[]
["CVE.U2.E4.PC1",null,"understand and distinguish the different types of image processing methods, namely, analogue and digital image processing."]
[]
["Number","Correct?","Question"]
[]
[1,"B","What is the output of the digital sensor?","A","display"]
[null,null,null,"B","digital image"]
[null,null,null,"C","array"]
[]
["CVE.U2.E4.PC2",null,"comprehends what sampling, quantization, and image interpolation are."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Sampling is related to coordinates values, and Quantitization is related to intensity values.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E4.PC3",null,"understands the differences between image resize and remapping."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Which one is an example of image remapping?","A","rotation"]
[null,null,null,"B","change of perspective"]
[null,null,null,"C","restauration of holes"]
[null,null,null,"D","Morphing"]
[]
["CVE.U2.E4.PC4",null,"understands what aliasing is and is able to distinguish between spatial aliasing and anti-aliasing."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Aliasing comes in several forms:​","A","Spatial aliasing in pictures, temporal aliasing in video and temporal aliasing in film/video"]
[null,null,null,"B","Spatial aliasing in photos and temporal aliasing in film/video"]
[null,null,null,"C","Spatial aliasing in pictures and temporal aliasing in film/video"]
[]
["CVE.U2.E4.PC5",null,"understands and defines spatial domain methods and frequency domain methods."]
[]
["Number","Correct?","Question"]
[]
[1,"A","One example of a spatial domain method is","A","Suprathreshold Contrast Sensitivity"]
[null,null,null,"B","Fourier Transform"]
[]
["CVE.U2.E4.PC6",null,"knows important concepts for image enhancement in spatial domain like point processing and mask processing"]
[]
["Number","Correct?","Question"]
[]
[1,"B","Point Processing is","A","where the values of the mask coefficients control the nature of the ​process."]
[null,null,null,"B","enhancement of any point in an image depends on only the gray-level at that ​point."]
[]
[2,"A","Mask Processing/Filtering is where the values of the mask coefficients control the nature of the ​process.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E4.PC7",null,"understands what contrast stretching is."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Contrast Stretching enhance the contrast in an image by stretching the range of intensity ​values to span a desired range of values.","A","True"]
[null,null,null,"B","False"]
[]
[2,"B","A contrast stretch does not improves the brightness differences accross the image","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E4.PC8",null,"knows some commonly used transformation functions used in contrast enhancement.​"]
[]
["Number","Correct?","Question"]
[]
[1,"B","Suprathreshold Contrast Sensitivity is a function from","A","Contrast Strech"]
[null,null,null,"B","Contrast Enhancement"]
[]
[2,"A,B,C,D","Which one are examples from contrast enhancement functions?","A","The Method for Gray Images"]
[null,null,null,"B","Suprathreshold Contrast Sensitivity"]
[null,null,null,"C","Images Negatives"]
[null,null,null,"D","Piecewise-Linear Transformations"]
[null,null,null,"E","Fourier Transform"]
[]
["CVE.U2.E4.PC9",null,"understands what histogram and histogram processing is and knows how histogram equalization works."]
[]
["Number","Correct?","Question"]
[]
[1,"A","histogram corresponds to a discrete function h(rk)=nk","A","True"]
[null,null,null,"B","False"]
[]
[2,"B","Histogram equalization can be defined as ","A","the statistical distribution of the gray levels ​in the image but contains no spatial information about the image"]
[null,null,null,"B","a method that increases the dynamic range of the gray-levels ​in a low-contrast image to cover full range of gray-levels."]
[]
["CVE.U2.E4.PC10",null,"understands how Singular Value Decomposition (SVD) and Singular Value Equalization (SVE) works."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Which is the technique that converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs the image in the spatial domain by using the updated singular value matrix?","A","SVD"]
[null,null,null,"B","SVE"]
[]
["CVE.U2.E4.PC11",null,"knows arithmetic and logic operations and understands their differences"]
[]
["Number","Correct?","Question"]
[]
[1,"A","c = a + b is ","A","Basic Arithmetic Operations"]
[null,null,null,"B","Basic Logic Operations"]
[]
["CVE.U2.E4.PC12",null,"understands image subtraction and image averaging process."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Image subtraction has several interesting applications, including ","A","correcting uneven illuminance"]
[null,null,null,"B","comparing images"]
[null,null,null,"C","nice effects"]
[]
["CVE.U2.E4.PC13",null,"understands the differences between linear and nonlinear spatial filtering."]
[]
["Number","Correct?","Question"]
[]
[1,"A","linear spatial filtering can be done with a convolution","A","True"]
[null,null,null,"B","False"]
[]
[2,"B","An  example of a non-linear filter is","A","convolution"]
[null,null,null,"B","sliding median"]
[null,null,null,"C","Fourier"]
[]
["CVE.U2.E4.PC14",null,"define and differentiate convolution and correlation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","A measure of similarity between two signals is","A","correlation"]
[null,null,null,"B","convolution"]
[]
[2,"A","convolution is a measure of effect of one signal on the other.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E4.PC15",null,"understands the differences between linear and nonlinear smoothing filters as well as statistical filters."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Smoothing Spatial Filters is known as averaging filter or lowpass filters, the types are linear and nonlinear.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E4.PC16",null,"to define and understand Laplacian, gradient and sobel operator."]
[]
["Number","Correct?","Question"]
[]
[1,"B","In Laplacian, if the center coefficient of the Laplacian mask is ","A","positive"]
[null,null,null,"B","negative"]
[]
[2,"A","Sobel operator consists of two masks to determine the edge in vector form.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E5.PC1",null,"understands the need, definition, and purpose of dimensionality reduction."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Dimension reduction is ","A","the process of reducing the number of variables/features in review"]
[null,null,null,"B","can ​be divided into two subcategories called Feature Selection and Feature Extraction"]
[null,null,null,"C","is the process of selecting a subset of relevant features or variables from the original set of features"]
[]
["CVE.U2.E5.PC2",null,"list the different types of dimensionality reduction."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Which ones are types of dimensionality reduction?","A","Feature Extraction"]
[null,null,null,"B","Feature Selection and Feature Extraction"]
[null,null,null,"C","Feature Selection "]
[]
["CVE.U2.E5.PC3",null,"define feature selection and understand its particularities."]
[]
["Number","Correct?","Question"]
[]
[1,null,"Feature selection is the process of selecting a subset of relevant features or variables from the original set of features.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E5.PC4",null,"knows the different feature selection strategies."]
[]
["Number","Correct?","Question"]
[]
[1,"D","Some feature selection strategies are:","A","Wrappers, Filters"]
[null,null,null,"B","Embedded "]
[null,null,null,"C","Hybrid and ensemble"]
[null,null,null,"D","Wrappers, Filters, Embedded, Hybrid and ensemble"]
[]
["CVE.U2.E5.PC5",null,"define feature extraction and understand its particularities."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Feature extraction is","A"," the process of transforming or projecting a space composing of many dimensions into ​a space of fewer dimensions."]
[null,null,null,"B","the reduction ​of higher vector spaces to lower orders through projection."]
[]
["CVE.U2.E5.PC6",null,"knows the different feature extraction strategies."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Is Principle Components Analysis a feature extraction strategy?","A","Yes"]
[null,null,null,"B","No"]
[]
["CVE.U2.E5.PC7",null,"identify and understand the differences between feature selection and feature extraction."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Summarize the dataset by creating linear combinations of the features.","A","Feature extraction"]
[null,null,null,"B","Feature selection"]
[]
[2,"A","Strong against irrelevant features.","A","Feature selection"]
[null,null,null,"B","Feature extraction"]
[]
["CVE.U2.E6.PC1",null,"The student understands the definition, purpose and need of image segmentation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Image segmentation is based on certain criteria to divide an input image into a number of the same nature of the category in order to extract the area which people are interested in.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E6.PC2",null,"The student knows the different types of image segmentation algorithms."]
[]
["Number","Correct?","Question"]
[]
[1,"C","Types of image segmentation algorithms","A","Region-based Segmentation​"]
[null,null,null,"B","Edge Detection Segmentation, Segmentation based on ​clustering"]
[null,null,null,"C","Region-based Segmentation​, Segmentation based on weakly-supervised learning in CNN, Edge Detection Segmentation and Segmentation based on ​clustering"]
[]
[]
["CVE.U2.E6.PC3",null,"The student can define region-based segmentation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Region- based segmentation ","A","uses pixel values from objects it could be a simple way to segment these different objects."]
[null,null,null,"B","creates a pixel-wise mask for each object in the image."]
[]
["CVE.U2.E6.PC4",null,"The student knows the differences between threshold segmentation and regional growth segmentation."]
[]
["Number","Correct?","Question"]
[]
[1,"B","The basic idea of the             algorithm is to combine the pixels with similar properties to ​form the region","A","Threshold Segmentation"]
[null,null,null,"B","Regional growth segmentation"]
[]
["CVE.U2.E6.PC5",null,"The student defines edge detection segmentation."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Edge detection segmentation refers to the use of equal regions of the pixel gray or color ​discontinuity detection area of the edge in order to reach image segmentation.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E6.PC6",null,"The student knows and differentiates the sobel and Laplacian operators."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Sobel operator is used to calculate the approximation of the ​gradient of the image luminance function, and it is based on the first derivative.​","A","True"]
[null,null,null,"B","False"]
[]
[2,"A,B","The Laplacian operator","A","needs to perform low-pass ​filtering before detecting the edge.​"]
[null,null,null,"B","is more suitable for digital image processing"]
[]
["CVE.U2.E6.PC7",null,"The student is able to define segmentation based on clustering."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Segmentation based on clustering is used to segment the pixels in the image space with the ​corresponding feature space points.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E6.PC8",null,"The student defines segmentation based on weakly-supervised learning in CNN."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Segmentation based on weakly-supervised learning in CNN","A","is based on the similarity between things as the criterion of class ​division"]
[null,null,null,"B","refers to the problem of assigning a semantic label to every ​pixel in the image and consists of three parts. "]
[]
["CVE.U2.E6.PC9",null,"The student knows the different use-cases of each image segmentation algorithm."]
[]
["Number","Correct?","Question"]
[]
[1,null,"Are Traffic Control Systems, Self Driving Cars​, Locating objects in satellite images use-cases of image segmentation?","A","Yes"]
[null,null,null,"B","No"]
[]
["CVE.U2.E6.PC10",null,"The student is able to select the most fitting image segmentation algorithm and assess its behaviour.​"]
[]
["Number","Correct?","Question"]
[]
[1,"A","The advantage of K-Means clustering algorithm ","A","is the simplicity and speed of the algorithm, and it is highly​ efficient and scalable for large data sets."]
[null,null,null,"B","is only applicable to the data set"]
[]
["CVE.U2.E7.PC1",null,"The student can define and understand image classification"]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Image classification can be defined as ","A","the prediction of a specific class, or label, for something that is defined by ​a set of data points."]
[null,null,null,"B","a subset of the classification problem, where an entire ​image is assigned a label."]
[null,null,null,"C","refers to the problem of assigning a semantic label to every ​pixel in the image and consists of three parts. "]
[]
["CVE.U2.E7.PC2",null,"The student is able to analyse and explore practical applications of image classification."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Which one is an example of practical applications of image classification?","A","Labeling an x-ray as cancer or not"]
[null,null,null,"B","Classifying a handwritten digit and Assigning a name to a photograph of a face.​"]
[]
["CVE.U2.E7.PC3",null,"The student knows different image classification models."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","What are the image classification models?","A","VGG-16"]
[null,null,null,"B","Res-Net-50"]
[null,null,null,"C","Inception"]
[null,null,null,"D","CNN"]
[null,null,null,"E","MNIST"]
[]
["CVE.U2.E7.PC4",null,"The student is able to select the image classification model that best fits a specific situation or problem."]
[]
["Number","Correct?","Question"]
[]
[1,null,"EfficientNet is the model with high accuracy.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E7.PC5",null,"The student is able to assess the performance of the classification model and proceed with the necessary measures."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Which of the models has the  following characteristic: \"The introduction of the 1 * 1 convolution operation drastically reduces the parameters\"? ​","A","Inception"]
[null,null,null,"B","ResNet50"]
[null,null,null,"C","VGG-16"]
[null,null,null,"D","EfficientNet"]
[]
["CVE.U2.E8.PC1",null,"The student knows the differences between working with image and video."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","What is a video?","A","is much larger object than an image."]
[null,null,null,"B","is a series of images played in sequence at a specified frame rate."]
[null,null,null,"C","is a 2-D array of numbers (intensity values,  gray levels)"]
[]
["CVE.U2.E8.PC2",null,"The student understands video capturing."]
[]
["Number","Correct?","Question"]
[]
[1,null,"Video capture can be described as the process of converting an analog video signal like ones that are ​produced by a video camera and sending it to local storage ​or to external circuitry.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E8.PC3",null,"The student knows how to perform video analysis and manipulation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Video analysis","A","refers to detect all interesting objects in video, identify their properties including human ​pose estimation."]
[null,null,null,"B","targets digital video using video processing and​ video editing techniques."]
[]
[2,"D","Accoridng to The Washington Post's how many forms of video manipulation exists?","A",3]
[null,null,null,"B",4]
[null,null,null,"C",5]
[null,null,null,"D",6]
[]
["CVE.U2.E9.PC1",null,"The student is able to define a feature."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Feature can be defined","A","as a piece of information about the content of an image, usually about ​whether a certain region of the image has certain properties."]
[null,null,null,"B","a set of frames."]
[]
["CVE.U2.E9.PC2",null,"The student knows the different types of image features."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Some examples of different types of image features are ","A","Harris corner"]
[null,null,null,"B","SIFT"]
[null,null,null,"C","SURF"]
[null,null,null,"D","Fourier Transform"]
[]
["CVE.U2.E9.PC3",null,"The student is able to define feature detection and matching and understand their purposes."]
[]
["Number","Correct?","Question"]
[]
[1,"A","In feature based image matching, distinctive features in images are detected and represented by feature descriptors.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E9.PC4",null,"The student knows some commonly used feature detectors and their classification."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Some examples of Feature detectors are","A","Harris corner"]
[null,null,null,"B","Tomasi's good feature"]
[null,null,null,"C","SIFT Descriptors"]
[]
["CVE.U2.E10.PC1",null,"The student is able to define object detection and tracking as well as understand their differences."]
[]
["Number","Correct?","Question"]
[]
[1,"B","What is object detection?","A","is identifying a certain class of image and then detect and tabulate their appearance in an image or video"]
[null,null,null,"B"," is finding all objects of certain classes in an image."]
[null,null,null,"C","is following or tracks an object once it is detected."]
[]
["CVE.U2.E10.PC2",null,"The student understands the machine learning and deep learning approaches for object detection and is able to select the best approach according to a specific problem or situation."]
[]
["Number","Correct?","Question"]
[]
[1,"A","CNN when applied to object detection problem, the result is an increased performance.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E10.PC3",null,"The student understands the process of object tracking and knows the classification of traditional methods for tracking objects."]
[]
["Number","Correct?","Question"]
[]
[1,"B","How many levels are of object tracking?","A",1]
[null,null,null,"B",2]
[null,null,null,"C",3]
[]
[2,"A","The Hungarian algorithm and Kalman filter are used for smoothing out the ​trajectories and predicting the locations of previously detected objects in the current frame.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E10.PC4",null,"The student is able to define target representation and localization algorithms and list some examples."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Do bounding boxes help to represent the location of an object?","A","True"]
[null,null,null,"B","False"]
[]
[2,"C","In object localization, the algorithm ​predicts a set of continuous numbers, that is                               , to draw a​ bounding box around an object of interest.​","A","x coordinate, y coordinate"]
[null,null,null,"B","height, and width"]
[null,null,null,"C","x coordinate, y coordinate, height, and width"]
[]
["CVE.U2.E10.PC5",null,"The student is able to define filtering and data association algorithms and list some examples"]
[]
["Number","Correct?","Question"]
[]
[1,"A,C","Some example of a data association algorithms are","A","hungarian algorithm"]
[null,null,null,"B","Fourier Transform"]
[null,null,null,"C","Kuhn Munkres algorithm"]
[]
[2,"B","Data association algorithms look for matches that decline certain match criteria and are subject to physical conditions. ","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E10.PC6",null,"The student understands the differences between target representation and localization algorithms and filtering and data association algorithms."]
[]
["Number","Correct?","Question"]
[]
[1,"B","                    is the process of matching information about newly observed ​objects with information that was previously observed about them.","A","Filtering"]
[null,null,null,"B","data association"]
[null,null,null,"C","target representation"]
[null,null,null,"D","localization"]
[]
["CVE.U2.E10.PC7",null,"The student is able to define and understand what facial recognition systems are as well as identify the applications of these systems."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Facial Recognition is ","A","a subpart of object detection where the primary object being detected is ​the human face."]
[null,null,null,"B","is the reconstruction of faded and old image hard copies ​that have been captured and stored."]
[]
[2,"A","Automated surveillance​, Monitoring closed circuit television​, and Image database investigations are some examples of the facial recognition applications.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E10.PC8",null,"The student knows the different techniques for face acquisition."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Which one is a technique for face acquisition?","A","Feature extraction"]
[null,null,null,"B","Contrast Enhancement"]
[null,null,null,"C","Images Negatives"]
[null,null,null,"D","Piecewise-Linear Transformations"]
[null,null,null,"E","Fourier Transform"]
[]
["CVE.U2.E10.PC9",null,"The student understands the motion estimation problem and knows the different methods and algorithms for finding motion vectors."]
[]
["Number","Correct?","Question"]
[]
[1,"A","Motion estimation examines the movement of objects in an image sequence to try to obtain ​vectors representing the estimated motion.","A","True"]
[null,null,null,"B","False"]
[]
[2,"B","                     describes the intrinsic ambiguity of perceiving the motion of an object ​through a local observation.","A","motion analysis"]
[null,null,null,"B","aperture problem"]
[]
["CVE.U2.E10.PC10",null,"The student is able to define the need and purposes of scene understanding in typical computer vision problems."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Scene understanding has the goal of build a machine that can't see like humans to automatically interpret ​the content of the images.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U2.E10.PC11",null,"The student understands the need, definition, and applications of 3D reconstruction."]
[]
["Number","Correct?","Question"]
[]
[1,"A","The objective of multi-view 3D reconstruction is ","A","to infer 3D geometry from a set of 2D images by ​inverting the image formation process "]
[null,null,null,"B","Recover image motion directly from spatio-temporal image brightness variations​"]
[null,null,null,"C","defined as a combination of many subtasks, specially object detection, ​tracking, and segmentation, and pose estimation.​"]
[]
["CVE.U2.E10.PC12",null,"The student knows the active and passive methods for 3D reconstruction and understand their differences."]
[]
["Number","Correct?","Question"]
[]
[1,"A","The difference between active methods and passive methods is that passive methods do not ​interfere with the object.","A","True"]
[null,null,null,"B","False"]
["CVE.U3.E1.PC1",null,"Know some of the most commonly used computer vision tools."]
[]
["Number","Correct?","Question"]
[]
[1,"B,C,D,E","Some computer vision tools are:","A","Excel"]
[null,null,null,"B","OpenCV"]
[null,null,null,"C","TensorFlow"]
[null,null,null,"D","MATLAB"]
[null,null,null,"E","CUDA"]
[]
["CVE.U3.E1.PC2",null,"Correctly install the mentioned programs."]
[]
["Number","Correct?","Question"]
[]
[1,"B","Is the installation of MATLAB free?","A","Yes"]
[null,null,null,"B","No, it's needed a license"]
[]
["CVE.U3.E1.PC3",null,"Know the operation of each program and its main functionalities. "]
[]
["Number","Correct?","Question"]
[]
[1,"A,B","Object/feature detection is a functionality of ","A","OpenCV"]
[null,null,null,"B","MATLAB"]
[]
[2,"A","It is an open-source library that is written specifically for real-time computer vision.","A","BoofCV "]
[null,null,null,"B","CUDA"]
[]
["CVE.U3.E1.PC4",null,"Handle programs easily and intuitively. "]
[]
["Number","Correct?","Question"]
[]
[1,"A","CUDA is used for processing by software engineers and has the Nvidia Performance Primitives library ​that contains various functions for image, signal, and video processing.​","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U3.E2.PC1 ",null,"Know the different application domains of computer vision."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C,D","Some examples of applications domains of computer vision are","A","Retail"]
[null,null,null,"B","Healthcare"]
[null,null,null,"C","Insurance"]
[null,null,null,"D","Metrology"]
[]
["CVE.U3.E2.PC2",null,"Explore and analyse several examples and applications of computer vision. "]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Some examples of computer vision techniques in retail are","A","track customers’ movements "]
[null,null,null,"B","recognize faces and determine human ​characteristics"]
[null,null,null,"C","find when someone is hiding an item in their ​backpack"]
[null,null,null,"D","predictive maintenance"]
[]
["CVE.U3.E2.PC3",null,"Recognize the challenges surrounding computer vision approaches. "]
[]
["Number","Correct?","Question"]
[]
[1,"A","Is Privacy and Ethics a challenge?","A","Yes"]
[null,null,null,"B","No"]
[]
[2,"A","By accessing powerful data centres, anyone is capable of creating fake images, videos or text content.​ Is this a challenge?","A","Yes"]
[null,null,null,"B","No"]
["CVE.U3.E2.PC4",null,"Identify and recognize computer vision intervention areas."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","Some intervation areas of computer vision are","A","Tesla Autopilot"]
[null,null,null,"B","Face recognition at ​Facebook"]
[null,null,null,"C","Amazon Go"]
[]
["CVE.U3.E3.PC1",null,"Realize the differences between the various state-of-the-art tools studied and select the best tool given a specific situation or problem."]
[]
["Number","Correct?","Question"]
[]
[1,"A","TensorFlow is a software library for numerical computation using data flow graphs.","A","True"]
[null,null,null,"B","False"]
[]
[2,"C","              is important because of its Speed, High accuracy, and Learning capabilities.​","A","CUDA"]
[null,null,null,"B","MATLAB"]
[null,null,null,"C","YOLO"]
[null,null,null,"D","BoofCV"]
[]
["CVE.U3.E3.PC2",null,"Use state-of-the-art tools to develop solutions for computer vision problems."]
[]
["Number","Correct?","Question"]
[]
[1,null,"One of the problems of computer Vision is the limit hardware.","A","True"]
[null,null,null,"B","False"]
[]
["CVE.U3.E3.PC3",null,"Carefully test and evaluate the solutions developed and make the necessary changes to achieve the best solution possible."]
[]
["Number","Correct?","Question"]
[]
[1,"A,B,C","When you try to choose the best solution to your problem you have to consider      .","A","Visual Programming"]
[null,null,null,"B","Flexibility"]
[null,null,null,"C","Deployment Management"]