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CS152 L3D Learning from Limited Labeled Data - HW2: SSL to the Moon
CS152Learning from Limited Labeled DataSSL to the MoonPythonMachine Learningsupervised training
In this HW2, you'll implement a common method for each style of SSL, (self- and semi-), and then evaluate your implementation on a toy dataset. Problem 1: Establish a baseline for supervised training on labeled-set-only. Problem 2: Can we gain value from pseudo-labeling? Problem 3: Can we gain value from SimCLR?
COMP5511 Artificial Intelligence Concepts - Assignment 1: TSP, GA, Dynamic Optimization and Multi-objective optimization
COMP5511Artificial Intelligence ConceptsTSPGADynamic optimization problemLarge-scale optimization problem
Traveling Salesman Problem (TSP) is a classical combinatorial problem that is deceptively simple. This problem is about a salesman who wants to visit n customers cyclically. In one tour, the salesman must visit each customer just once and should finish up where he started
COMP3702 Artificial Intelligence (Semester 2, 2024) Assignment 2: BeeBot MDP
COMP3702Artificial IntelligenceBeeBot MDPPython
You have been tasked with developing a planning algorithm for automatically controlling BeeBot, a Bee which operates in a hexagonal environment, and has the capability to push, pull and rotate honey ‘Widgets’ in order to reposition them to target honeycomb locations. To aid you in this task, we have provided support code for the BeeBot environment which you will interface with to develop your solution. To optimally solve a level, your AI agent must efficiently find a sequence of actions so that every Target cell is occupied by part of a Widget, while incurring the minimum possible action cost.
CS6601 Artificial Intelligence - Assignment 3: Bayes Nets
Artificial IntelligenceBayes NetsPythonProbabilistic ReasoningQuantifying UncertaintyMarkov Chain
In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables.
EECS 492 Introduction to Artificial Intelligence: Designing Agents, Search Tree, Brick Sorting Machine, Heuristics and Hill Climbing
EECS492Introduction to Artificial IntelligenceDesigning AgentsSearch TreeBrick Sorting MachineHeuristics
Tic - tac - toe is a game for two players who take turns marking the spaces in a three - by - three grid with X or O. The game’s objective is to be the first in placing three of their markers in a horizontal, vertical, or diagonal row (see Figure 1). **Figure 1**: A game of Tic - Tac - Toe where the O player has won, as it has three markers in a diagonal row. You are now tasked with developing an AI algorithm for a tic - tac - toe agent. The agent is a robot that can play against another agent (human or robot) on a piece of paper using a pen.
COMPSCI 367 Artificial Intelligence Assignment 2: PDDL
COMPSCI 367Artificial IntelligencePDDLPrologvariable elimination algorithm
For this question, you are asked to solve a classical planning problem using PDDL. Read the problem description carefully. Polynesian navigators trained in schools (wānanga) to learn a body of wayfinding techniques that provided the skills necessary to travel to other locations throughout the Pacific, including over vast distances.
COMP9417 - Machine Learning Homework 2: Bias, Variance and an application of Gradient Descent
COMP9417Machine LearningPythonBiasVarianceGradient Descent
In this homework we revisit the notion of bias and variance as metrics for characterizing the behaviour of an estimator. We then take a look at a new gradient descent based algorithm for combining different machine learning models into a single, more complex, model.
Machine Learning Fundamentals Group Assessment: Model comparison
Machine LearningRMSEFeature EngineeringKNNRegression
Background Information Kevin is a professional real-estate manager. In the past, he relied on using a few important features for home valuation. His boss recently asked him to take the initiative to learn to use big data and machine learning algorithms to value home prices in order to better communicate with customers.
G6061 Fundamentals of Machine Learning Assignment: Photo Classification
G6061Fundamentals of Machine LearningPhoto ClassificationImage ClassificationPythonCNN
The data come from photos, and your task is to come up with a machine learning method for classifying the photos according to whether their content is happy or sad. The data you are given for each photo consists of 3456 features. 3072 of these were extracted from a deep Convolutional Neural Network (CNN) [1], and the remaining 384 are gist features [2]. (You are given all these features as a 1-dimensional array, so you will not be performing any feature extraction on raw images.)
BUSANA7003 Business Analytics Project Final Project: AQR Asset Management
BUSANA7003Business AnalyticsAQR Asset ManagementPythonMachine Learning
You are starting a new job as a Business Analyst at AQR Asset Management, a global investment management firm focused on quantitative investment strategies. Your first task is to analyse the performance of US-listed securities during the COVID-19 market crash.
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