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Computer Vision - 计算机视觉 Computer Organization and Architecture - 计算机组织与体系结构
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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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