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COMP4436 AIoT Assignment I - Comparative Analysis of ML, DL and SNN Algorithms in AIoT Applications

COMP4436AIoTPythonComparative AnalysisMLDL

The goal of this assignment is to implement and compare the performance of various machine learning (ML) and deep learning (DL) algorithms, including a spiking neural network (SNN), in the context of an Artificial Intelligence of Things (AIoT) application

COMP4436 Artificial Intelligence of Things - AIoT Assignment - Comparative Analysis of ML, DL and SNN Applications lgorithms in AIoT

HK PolyUCOMP4436Artificial Intelligence of ThingsComparative AnalysisSNN

The goal of this assignment is to implement and compare the performance of various machine learning (ML) and deep learning (DL) algorithms, including a spiking neural network (SNN), in the context of an Artificial Intelligence of Things (AIoT) application.

EECS 183: Elementary Programming Concepts - Final Project: Elevators

EECS183Elementary Programming ConceptsElevatorsC++

Elevators is a project that dabbles in Game Design, Artificial Intelligence, and designing real-world systems. Using C++, you will complete an implementation for a game in which the player operates 3 elevators in a busy building, making decisions and servicing requests to keep the people inside the building as happy as possible.

COMP3702 Artificial Intelligence - Assignment 3: Reinforcement Learning

COMP3702Artificial IntelligenceReinforcement LearningPythonPytorchDeep Q-Network (DQN)

In this assignment, you will implement Deep Reinforcement Learning algorithms and analyse their parameters and performance. This assignment will test your skills in training and understanding reinforcement learning algorithms for practical problems and understanding of key algorithm features and parameters.

JC4003: Natural Language Processing - Group Assessment: Understanding and Generating Explanations from the RuozhiBa Dataset

JC4003Natural Language ProcessingRuozhiBaData Annotation

In this group assessment, you will explore and experiment with traditional machine learning and deep learning models, including large language models (LLMs), to generate accurate meanings and explanations for the samples provided in the RuozhiBa dataset. The purpose of this exercise is to apply your knowledge from the course to a real-world dataset, practicing your skills in data annotation, model design, and evaluation.

CS 188 Introduction to Artificial Intelligence - Project 3: Reinforcement Learning

CS188Introduction to Artificial IntelligenceReinforcement LearningPythonValue IterationQ-Learning

In this project, you will implement value iteration and Q-learning. You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pacman.

COMP3411/9814 24T3 Artificial Intelligence - Assignment 1: Artificial neural networks

COMP3411COMP9814Artificial IntelligenceArtificial neural networksPython

In this assignment, you will use artificial neural networks for drought modelling in the Murray-Darling Basin. You will conduct two tasks: (a) A classification task to predict whether there is ‘a drought’ or ‘no drought’ based on the climate conditions. (b) A regression task to predict the intensity of a drought based on the climate conditions.

BFF5555 Financial machine learning - Project: Predict positive market movements

BFF5555Financial machine learningPredict positive market movementsBinary ClassificationHyperparameter TuningCross Validation

You are required to develop a machine learning model to predict positive market movements (uptrend). This prediction task will be treated as a binary classification problem, where the target variable is binary [0, 1].

CS112 Lab 09: Neural Networks

CS112Introduction to CS IIMachine LearningJavaNeural Networks

Later this semester, you will create a working neural network in Java, using only your own code. In later classes, you will probably use neural network libraries developed by others to learn about many facets of Machine Learning. But in this class, you will learn that there is no magic in making a neural network— it is something you can build yourself...though the fact that neural networks perform so well does seem like magic.

CS152 L3D Learning from Limited Labeled Data - HW1: Transfer Learning for the Birds

CS152Learning from Limited Labeled DataTransfer Learning for the BirdsPythonMachine Learning

In this HW1, you'll apply Transfer Learning to a real dataset, and wrestle with several questions: Problem 1: For a specific target classification task of interest, would we rather have a source model trained on a "generic" dataset like ImageNet1k, or a smaller dataset related to our target task? Problem 2: What are the tradeoffs between fine-tuning just the last layer (aka "linear probing") and fine-tuning a few more layers? Can we compose these to do better?

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