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CS 135 Intro to Machine Learning - Homework 2: Evaluating Binary Classifiers and Implementing Logistic Regression

CS135Intro to Machine LearningEvaluating Binary ClassifiersLogistic RegressionCancer-Risk ScreeningPython

In this HW, you’ll complete two problems related to binary classifiers. In Problem 1, you’ll implement common metrics for evaluating binary classifiers. In problem 2, you’ll learn how to decide if a new feature can help classify cancer better than a previous model. As much as possible, we have tried to decouple these parts, so you may successfully complete the report even if some of your code doesn’t work. Much of your analysis will use library code in sklearn with similar functionality as what you implement yourself.

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.)

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