Introduction to Machine Learning
CS-4410
- Major Electives (Choose two of the following courses)
- 3 tín chỉ
- Tiên quyết: CS 3323
89 tài liệu • Cập nhật: 06/09/2026
Thư mục tài nguyên
Assignments
40 file(s)
- midterm_Summer-2026
- Exam
- Linear regression
- cal_housing
- Nearest neighbor practice
- Assignment week 1
- Sylabus_explanation
- Assignment week2
- course Lecture 1
- i2ml4e-chap01 — [Introduction to Machine Learning]
- lecture1
- Solutions week2
- Assignment week 3
- Course lecture 2
- i2ml4e-chap02 — [Supervised Learning]
- Solutions week3
- assignment1
- CleanData
- Answer
- clean_data
- CS4410_Assignment1
- SuperCenterDataNew
- uncleandata
- assignment2
- Assignment2
- MyData
- assignment3
- Assignment3
- assignment4
- Assignment4
- final_Fall-2024
- final_Summer-2026
- midterm_Fall-2024
- midterm_Summer-2026
- ProjectAssignment
- ProjectAssignment
- Apriori_Algorithm_Implementation
- AprioriAlgorithm
- SuperCenterDataNew
- TestAprioriAlgorithm_ver2
Modules
30 file(s)
- Datalake_Big_data
- Chapter07Full
- Chapter08Full
- Chapter09Full
- Example of Multiple Linear Regression in Python - Data to Fish — [Python Multiple Regression]
- Linear _Regression_Lecture3 — [Bias-Variance]
- Linear_Regression_Lecture1
- regression-Lecture2 — [Multiple Regression]
- Example NN classification and regression
- K-NN — [Nearest Neighbor Regression]
- NN_examples_class and pred with accuracy metrics
- bayesNaive
- Chapter4.1_Naive Bayesian
- naive bayes code
- NeuralNetwork — [Neural Network Overview]
- perceptron with representations
- perceptron_weights
- Chapter-1_Introduction
- Chapter-2_Supervised-Learning
- Chapter-3_Bayesian-Decision-Theory
- Chapter-4_Parametric-Methods
- Chapter-6_Dimensionality-Reduction
- Chapter-7_Clustering
- Chapter-9_Decision-Trees
- Chapter-11_Multilayer-Perceptrons
- Chapter-13_Kernel-Machines
- Chapter-14_Graphical-Models
- Chapter-17_Combining-Multiple-Learners
- Chapter-19_Designing-and-Analysis-of-ML-Experiments
- Recommender-System
References
18 file(s)
- Ethem Alpaydin - Introduction to Machine Learning, 4th Edition (MIT Press, 2020)
- 5-2019 - Applied Soft ComputingQ1 mwe — [MWE Trend Prediction]
- conference-template-a4 (2) — [IEEE A4 Paper Template]
- IJACT4-5301JE — [Communication Network Isomorphism]
- Chapter1.1_Introduction to Machine Learning — [ML Introduction]
- Chapter1.2_ConceptLearning — [Concept Learning]
- Datawharehouse_Distribution — [Distributed Data Warehouse]
- Linear Regression using Gradient Descent _ by Adarsh Menon _ Towards Data Science — [Gradient Descent Regression]
- PSO — [Particle Swarm Optimization]
- GenerativeAI
- Introduction to Machine Learning (3rd Ed, 2014)
- Machine-Learning_Tom-M-Mitchell_1997
- Mining of Massive Datasets (3rd Ed, 2020)
- Probabilistic Machine Learning - Advanced Topics (2023)
- Review Final
- Review Midterm
- airline
- Sample of some useful functions