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CSC728 Machine Learning Assignment Example Malaysia

Machine Learning is a system of artificial intelligence that enables automated acquisition and integration. This course aims to introduce you to the basics in this field through Machine Learning which is specifically designed for postgraduate students interested in Artificial Intelligence or even Data Science careers.

In this course, we will explore the theory and application of machine learning. Topics include supervised as well as unsupervised models for classification problems that have emerged in recent years to more accurately predict outcomes based on large sets of data points or features like images with a high degree of complexity due to their nature not being able various aspects such things like voice recognition software etc.; there’s even work going on now regarding “natural language processing” which would allow computers themselves learn about us humans through our use (or misuse) them by gathering all sortsa information-writing emails state changes back then coming forth outta nowhere – pretty much anytime really.

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The main learning methods that is discussed in this course are:

(1) supervised learning,

(2) unsupervised learning,

(3) reinforcement learning.

The research in Machine Learning has developed into broad areas of AI, the four main thrusts of research are

  • the improvement of classification accuracy by learning ensembles of classifiers,
  • methods for scaling up supervised learning algorithms,
  • reinforcement learning, and
  • the learning of complex stochastic models.

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These are the questions that can be asked in CSC728 Machine Learning Assignment. By the end of this course, Malaysian students will be able to answer the following questions.

Students can also avail some other services that are group assignment, group projects, case study, individual assignment, business plans, etc.

Assignment Task 1: Apply the machine learning strategies design techniques in designing machine learning applications.

Machine learning strategies are used in the design of machine learning applications. The machine learning techniques are applied to solve new problems in designing an application, for example, to get better predictive models. Among the methods of domain knowledge, extraction and problem formulation is machine learning along with representation modeling to make a fully trained model suitable for the decision-making process.

To create our designs, we collect information from data sources such as databases and if necessary enter these manually as well. We also consider how we want our system (ML) to respond when it encounters inputs that it has never seen before — input values for which we do not know their corresponding output values — and what steps we need tackle this uncertainty effectively. There are many types of ML algorithms like supervised, unsupervised and reinforcement learning etc. But we mostly use supervised machine learning algorithms in different applications to solve prediction problems.

Let’s take an example for better understanding of the concept: Assume that we want to build a mobile application which recommends us restaurants nearby, using user preferences such as “type of restaurant”, “cuisine type”, “preferred budget”, etc. as input variables and the corresponding ratings given to these restaurants by users as output variable. In this scenario, we can use supervised learning algorithm where labeled data is provided in the form of training dataset. The algorithm will learn from the training dataset and try to predict the rating for a restaurant, given its attributes (type of restaurant, cuisine type, preferred budget), as input.

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Assignment Task 2: Build a machine learning application using various types of machine learning strategies in solving complex problems.

There are a number of machine learning strategies that address different types of problems. Here are some examples — these definitions should be read with the explanations and comments at the bottom:

  • Supervised: A classifier learns from positive and negative samples marked for each class and predicts new instances of their label. For example, binary classification trains a network to predict whether an email is spam or not-spam given its features such as sentiment, subject line content, number of words etc. In contrast, regression models try to predict a numerical value based on many input variables. 
  • Unsupervised: A clusterer analyses unlabeled data into clusters where objects in those clusters exhibit properties distinct from those in other clusters. The goal is to find inherent structures in data, but no classification of examples is required.
  • Semi-supervised: A classifier learns from labeled and unlabeled data. Self-training networks can prioritize their learning on the more informative samples. For example, a self-trained network predicts which emails are spam or not based on its own prioritization of labeled data. The advantage is that unlabeled data can be used to improve performance and the number of training samples can be increased.
  • Reinforcement learning: A program or agent learns how to achieve a goal through trial-and-error interactions with an environment. For example, AlphaGo Zero learned how to play Go by playing against itself over and over again.
  • Anomaly detection: This task detects rare items or events in data streams, which are not easily predictable using standard models. For example, a system that detects fraudulent credit card transactions would be an anomaly detector. It is important to note that the threshold for what constitutes an “anomaly” can be subjective and may vary over time.

When it comes to big data, there are many different types of problems that need to be solved. Different machine learning strategies can be used in order to address these problems. Supervised learning is a method where a classifier learns from positive and negative samples marked for each class. This type of learning is often used for binary classification, where a network is trained to predict whether an email is spam or not-spam given its features. In contrast, regression models try to predict a numerical value based on many input variables.

Assignment Task 3: Design machine learning based on the existing machine learning strategies design techniques.

In addition to recognizing that all machine learning strategies are equal, designing a new machine learning model should be done with respect for the following design principles: 

  • Time-sensitive Design Principle: This is a “no joke” principle. If you want to be able to predict accurately in the future, then models need to give humans enough time and information delay. It’s hard enough as it is for humans to make predictions about what will happen in the future or how they’ll feel at some point down the road than trying to do so with limited time and information delay would just mean bad results.
  • Massively Parallel Processes Design Principle: This is where we can take advantage of current technology advancements such as GPUs and reduce computational costs incurred byrd when using high performance computers.
  • Hierarchical Modeling Design Principle: This principle is important as it allows for more manageable models and data. The idea is that by breaking a problem down into smaller, more manageable pieces, we can better understand the overall problem and find solutions more efficiently. This principle can be applied at various levels of granularity such as individual data points, clusters of data points, or entire datasets.
  • Robustness Design Principle: This principle is important as it allows us to build models that can withstand a variety of possible perturbations and errors. The idea is that we want our models to be flexible enough to cope with a range of potential issues that may arise during deployment.  
  • Model Transparency Design Principle: This principle is important for two reasons. First, it helps explain the inner workings of machine learning models to make them understandable by humans who are not necessarily data scientists or computer scientists (e.g., business users). Second, transparency allows us to understand machine learning models beyond a single use case and can be leveraged as a debugging tool.

When designing a new machine learning model, it is important to keep in mind that all machine learning strategies

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