MLNotes and tutorials

Updated

Machine Learning (ML)
4. Semester: Computer Science
 

Machine Learning Landscape by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons ML Landscape Agenda Introduction to ML Landscape (Word) Introduction to ML Landscape (PDF)	Machine Learning 1 Slides (PP) Machine Learning 1 Slides (PDF)	What is Machine Learning Simple Machine Learning Introduction (12 minutes) . Video from lessons ML Landscape Agenda 2022.02.03 ML Landscape No. 1 Overview 2022.02.03 ML Landscape No. 2 Models&Types 2022.02.03 ML Landscape No. 3 Challenges 2022.02.03 Phyton1 and Panda Solutions Explained 2022.02.10 Link MS Streams	Chapter 1 Assignments: No. 1 - 14 Ananconda Installation Guide Python Basic No. 1   Jupyter Introduction Panda Introduction Importing DataSet Housing
		Tutorials/Videos Not Used
		Video from former lessons 2022 Autumnn ML Landscape Agenda 2021.08.26 ML Landscape No. 1 Overview 2021.08.26 ML Landscape No. 2 Models&Types 2021.08.26 ML Landscape No. 3 Challenges 2021.08.26 Phyton1 and Panda Solutions Explained 2021.09.02 Video from former lessons 2021 Spring ML Landscape No. 1 Overview 2021.01.28 ML Landscape No. 2 Models&Challenges 2021.01.28 ML Landscape No. 3 Challenges Old version 2020.08.29 Phyton1 and Panda Solutions Explained 2021.02.01 Video from former lessons 2020 Autumn ML Landscape No. 1 Overview 2020.08.29 ML Landscape No. 2 Models 2020.08.29 ML Landscape No. 3 Challenges 2020.08.29

End-To-End Machine Learning Project Part 1 by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons ML End-To-End Project No. 2A Agenda ML End-To-End Project No. 2A (Word) ML End-To-End Project No. 2A (PDF)	ML Chapter 2A Slides (PP) ML Chapter 2A Slides (PDF)	Linear Regression (13 minutes) ML Cross Validation . Video from lessons 2022 Spring ML End-To-End Agenda No. 1 2022.02.10 ML End-To-End No. 1 Explore Data 2022.09.10 ML End-To-End No. 2 Code Vizualization 2022.02.10 Introduction Linear Regression Exercise 2022.02.10 Link MS Streams	Chapter 1 Assignments: No. 1 - 14 Python Basic No. 2 Linear Regression Housing Ch. 2 No. 1
		Tutorials/Videos Not Used
		Video from former lessons 2021 Autumn ML End-To-End Agenda No. 1 2021.09.02 ML End-To-End No. 1 Explore Data 2021.09.02 ML End-To-End No. 2 Code Vizualization 2021.09.02 Introduction Linear Regression Exercise 2021.02.01 Video from former lessons 2021 Spring ML End-To-End Agenda No. 1 2021.02.01 ML End-To-End No. 1 Explore Data 2021.02.01 ML End-To-End No. 2 Code Vizualization 2021.02.01 Introduction Linear Regression Exercise 2021.02.01 Video from former lessons 2020 Autumn ML End-To-End No. 1 Explore Data 2020.09.02 ML End-To-End No. 2 Vizualization 2020.09.02

End-To-End Machine Learning Project Part 2 by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used or to be viewed	Assignments
Overview of lessons ML End-To-End Project No. 2B Agenda ML End-To-End Project No. 2B (Word) ML End-To-End Project No. 2B (PDF) ML End-To-End Project No. 2C Agenda Machine Learning Big Picture Ch 2	ML Chapter 2B Slides (PP) ML Chapter 2B Slides (PDF) ML Chapter 2C Slides (PP) ML Chapter 2C Slides (PDF)	Linear Regression (9 minutes) ML Cross Validation (6 minutes) . Video from lessons 2022 Spring ML End-To-End Agenda No. 2 2022.02.17 ML End-To-End Agenda No. 3 2022.02.24 Linear Regression Solution Explained 2022.02.17 Regression Performance Solution Explained 2022.02.17 Linear Regression Standard Solution Explained 2022.02.24 ML End-To-End No. 3 Prepare Data 2022.02.17 ML End-To-End No. 4 Training Models 2022.02.24 Link MS Streams	Housing Ch. 2 No. 1 Regression Performance Linear Regression Standard Linear Regression Missing Data Housing Ch. 2 No. 2
		Tutorials/Videos Not Used
		Video from former lesson 2021 Autumn ML End-To-End Agenda No. 2 2021.09.09 ML End-To-End Agenda No. 3 2021.09.16 Linear Regression Solution Explained 2021.09.09 Regression Performance Solution Explained 2021.09.09 Linear Regression Standard Solution Explained 2020.09.16 ML End-To-End No. 3 Prepare Data 2021.09.09 ML End-To-End No. 4 Training Models 2021.09.16 Video from former lessons 2021 Spring ML End-To-End Agenda No. 2 2021.02.08 Linear Regression Solution Explained 2021.02.08 Regression Performance Solution Explained 2021.02.08 Linear Regression Standard Solution Explained 2021.02.15 ML End-To-End No. 3 Prepare Data 2020.02.08 ML End-To-End No. 4 Training Models 2020.02.15 Video from former lessons Linear Regression Solution Explained 2020.09.09 Regression Performance Solution Explained 2020.09.09 Linear Regression Standard Solution Explained 2020.09.16 ML End-To-End No. 3 Prepare Data 2020.09.09 ML End-To-End No. 4 Training Models 2020.09.16

Classification by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used / Or to be viewed	Assignments
Overview of lessons Training and Classification Agenda ML Classification (Word) ML Classification (PDF)	ML Chapter 3 Slides (PP) ML Chapter 3 Slides (PDF)	YouTube: ML Specifity & Sensivity (11 minutes) YouTube. ML Confusion Matrix (13 minutes) YouTube: ML ROC-AUC (20 minutes) . Video from lessons Training and Classification Agenda 2022.02.24 Classification No, 1 2022.02.24 Classification No. 2 2022.02.24 Link MS Streams	Classification Chapter 3 Questions Classfication MNIST Exercise
		Tutorials/Videos Not Used
		Video from former lessons 2021 Spring Training and Classification Agenda 2021.02.15 Classification No, 1 2021.02.15 Classification No, 2 2020.09.09 N/A See from 2020r Video from former lessons Classification No, 1 2020.09.16 Classification No, 2 2020.09.16

Training Models by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons Training Models Agenda Introduction to Training (Linear) Models (Word) Introduction Training (Linear) Models (PDF)	ML Chapter 4 No 1 Slides(PDF) ML Chapter 4 No 2 Slides(PDF) ML Chapter 4 No 3 Slides(PDF) ML Chapter 4 No 1 Slides(PP) ML Chapter 4 No 2 Slides(PP) ML Chapter 4 No 3 Slides(PP)	Gradient Descent (23 min) Stochastic Gradient Descent (11 minutes) Video from lessons Training Models Agenda 2022.03.17 Training Models No. 1 Introduction 2022.03.17 Training Models No. 2 Gradient Descent A 2022.03.17 Training Models No. 3 Gradient Descent B 2022.03.17 Training Models No. 4 Learning Principles 2022.03.17 Link MS Strreams	Training Chapter 4 Questions Training Model Exercise
		Tutorials/Videos Not Used
		MIT Traditional Blackboard Lesson(44 minutes) It starts slow but THEN..... Video from former lessons 2021 Autumn Training Models Agenda 2021.10.07 Training Models No. 1 Introduction 2021.10.07 Training Models No. 2 Gradient Descent A 2021.10.07 Training Models No. 3 Gradient Descent B 2021.10.07 Training Models No. 4 Learning Principles 2021.10.07   Video from former lessons 2021 Spring Training Models Agenda 2021.03.15 Training Models No. 1 Introduction 2021.03.15 Training Models No. 2 Gradient Descent A 2021.03.15 Training Models No. 3 Gradient Descent B 2021.03.15 Training Models No. 4 Learning Principles 2021.03.15   Video from former lessons removed (low sound quality) Training Models No. 1 Introduction Training Models No. 2 Gradient Descent Training Models No. 3 Learning Principles

Logistic Regression by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons Logistic Regression Agenda Introduction to Logistic Regression (Word) Introduction to Logistic Regression (PDF)	Logistic Regression 1 Slides (PP) Logistic Regression 1 Slides (PDF) Logistic Regression 2 Slides (PP) Logistic Regression 2 Slides (PDF)	Logistic Regression Introduction (8 minutes) Donald Duck and Math 1959 . Video from lessons Logistic Regression Agenda 2022.03.24 Logistic Regression No. 1 Introduction 2022.03.24 Logistic Regression No. 2 Video Mouse Weight 2022.03.24 Logistic Regression No. 3 Basic Formulae 2022.03.24 Logistic Regression No. 4 Iris Case & Code 2022.03.24 Link MS Strreams	Logistic Regression Questions Chapter 4 Logistic Regression Iris Exercise Logistic Regression Iris Program
		Tutorials/Videos Not Used
		Logistic Regression Details (17 minutes) Video from former lessons 2021 Autumn Logistic Regression Agenda 2021.10.14 Logistic Regression No. 1 Introduction 2021.10.14 Logistic Regression No. 2 Video Mouse Weight 2021.03.22 Logistic Regression No. 3 Basic Formulae 2021.10.14 Logistic Regression No. 4 Iris Case & Code 2021.10.14   Logistic Regression Agenda 2021.10.14 Logistic Regression No. 1 Introduction 2021.10.14 Logistic Regression No. 2 Video Mouse Weight 2021.03.22 Logistic Regression No. 3 Basic Formulae 2021.10.14 Logistic Regression No. 4 Iris Case & Code 2021.10.14 Video from former lessons 2021 Spring Logistic Regression Agenda 2020.03.22 Logistic Regression No. 1 Introduction 2021.03.22 Logistic Regression No. 2 Video Mouse Weight 2021.03.22 Logistic Regression No. 3 Basic Formulae 2021.03.22 Logistic Regression No. 4 Iris Case & Code 2021.03.22   Video from former lessons 2020 Autumn Logistic Regression No. 1 Introduction 2020.10.21 Logistic Regression No. 2 Basic Formulae 2020.10.21 Logistic Regression No. 3 Iris Case 2020.10.21 Video from former lessons 2020 Spring Logistic Regression No. 1 Basic 2020.04.02 Logistic Regression No. 2 Basic 2020.04.02 Logistic Regression No. 3 Iris Case 2020.04.02

Decision Trees by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons Decision Trees Agenda Introduction to Decision Tree (Word) Introduction to Decision Tree (PDF)	Decision Tree Slides (PP) Decision Tree Slides (PDF)	Decision Trees Introduction(16 minutes) Video from lessons Decision Trees Agenda 2022.03.31 Decision Tree No. 1 Basics 2022.03.31 Decision Tree No. 2 Learning & Training 2022.03.31 Decision Tree No. 3 Intro to Assignment 2021.04.12 (Danish) Decision Tree No. 4 Assignment Answers 2020.10.28 Link MS Strreams	Decision Trees Questions Chapter 6 Decision Trees Exercise
		Tutorials/Videos Not Used
		Decision Trees Missing Data (5 minutes) Video from former lessons 2021 Autumn Decision Trees Agenda 2021.10.27 Decision Tree No. 1 Basics 2021.10.27 Decision Tree No. 2 Learning & Training 2021.10.27 Decision TreeNo. 3 Intro to Assignment 2021.04.12 Decision Tree No. 4 Assignment Answers 2020.10.28 Video from former lessons 2021 Spring Decision Trees Agenda 2021.04.12 Decision Tree No. 1 Basics 2021.04.12 Decision Tree No. 2 Basics Learning 2021.04.12 Decision TreeNo. 3 Intro to Assignment 2021.04.12 Decision Tree No. 4 Assignment Answers 2020.10.28 Video from former lessons 2020 Autumn Decision Tree No. 1 Basics 2020.10.21 Decision Tree No. 2 Basics 2020.10.28 Decision Tree Intro to Assignment 2020.10.28 Decision Tree Assignment Answers 2020.10.28 Video from former lessons 2020 Spring Decision Tree No. 1 Basics 2020.04.02 Decision Tree No. 2 Basics 2020.04.07 Decision Tree Intro to Assignment 2020.04.07 Decision Tree Assignment Answers 2020.04.07

Support Vector Machine (SVM) by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons Introduction to SVM (Word) Introduction to SVM (PDF)	SVM Slides (PP) SVM Slides (PDF)	SVM Introduction (20 minutes) SVM Pylnomial Kernel (16 minutes) . Video from lessons SVM No. 1 Basics Theory and Code 2020.04.08 SVM No. 2 Exercise Solution Explained 2020.11.04 Link MS Streams	SVM Questions Chapter 5 SVM Iris Exercise SVM Iris Program
		Tutorials/Videos Not Used
		Video from former lessons 2020 Spring, NOT the best SVM No. 1 Basics 2020.04.08

Unsupervised Learning (UL) by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons Unsupervised Learning Agenda Introduction to UL (Word) Introduction to UL (PDF)	UL Slides (PP) UL Slides (PDF)	UL K-Means (8 minutes) Video from lessons Unsupervised Learning No. 1 Basics&Clustering 2022.04.07 Unsupervised Learning No. 2 K-Means & Code 2022.04.07 Unsupervised Learning No. 3 Code Solutions 2022.04.07 Link MS Streams	Unsupervised Learning Questions Chapter 9UL K-Means Exercise Unsupervised Learning Programs Ch. 9 Unsupervised Learning Customer Exercise Customer Dataset on Kaggle
		Tutorials/Videos Not Used
		Hierarcial Clustering (11 min) PCA Algorithm (21 min) Video from lessons 2021 Autumn Unsupervised Learning No. 1 Basics&Clustering 2021.11.03 Unsupervised Learning No. 2 K-Means & Code 2021.11.03 Video from former lessons 2021 Springs Unsupervised Learning No. 1 Basics&Clustering 2021.04.26 Unsupervised Learning No. 2 K-Means & Code 2021.04.26 Unsupervised Learning No. 3 Code Solutions 2021.04.26 Video from former lessons (removed) Unsupervised Learning No. 1 Basics&Clustering 2020.11.04 Unsupervised Learning No. 2 K-Means & Code 2020.11.04 Unsupervised Learning No. 3 Code Solutions 2020.11.04

Artificial Neural Networks (ANN) by Michael Claudius

Description/Subjects	Slideshow	Tutorials/Videos Used	Assignments
Overview of lessons ANN Agenda Introduction to ANN (Word) Introduction to ANN (PDF)	ANN Basics Slides (PP) ANN Basics Slides (PDF) MLP Slides (PP) MLP Slides (PDF)	Tutorials/Videos ANN 1. Introduction (19 min) ANN 2. Learning by Gradients (21 min) ANN 3. Backpropagation (13 min) ANN 4. Math Calculus !! (10 min) Video lessons ANN Agenda 2021.11.11 ANN No. 1 Basic Concepts 2021.11.11 ANN No. 2 MLP Introduction Video 3BlueBrown 2021.11.11 ANN No. 2. MLP Introduction and Code 2021.11.11 ANN No. 3. MLP Introduction Assignment 2020.04.03 ANN No. 4. Perceptron Solution 2021.11.11 Link MS Streams	ANN Questions Chapter 10 Tensorflow Installation Perceptron Iris Exercise ANN Programs Ch. 10 MLP Classfication Fashion Exercis Playground at Tensorflow Exercise
		Tutorials/Videos Not Used
		ANN 4. Math Calculus !! (10 min) Video from former lessons 2021 Spring ANN Agenda 2021.04.19 ANN No. 1 Basic Concepts 2021.04.19 ANN No. 2. MLP Introduction with Video and Code 2021.04.19 ANN No. 3. MLP Introduction Assignment 2020.04.03 ANN No. 4. Perceptron Solution 2021.04.19   Video from former lessons 2020 Autumn ANN No. 1 Basic Concepts 2020.11.11 ANN No. 2. MLP Introduction with Video and Code 2020.11.11 ANN No. 3. MLP Introduction Assignment 2020.04.03 ANN No. 4. Perceptron Solution 2020.11.11 Video from former lessons 2020 Spring ANN No. 1 Basic Concepts 2020.04.23 ANN No. 2. MLP Introduction 2020.04.23 ANN No. 3. MLP Introduction Assignment 2020.04.03

Socket Tutorials

Description/Subjects	Tutorials	Exercises
Theoretical Socekt Concepts Server and Socekt set up Introduction 0 Socket Concepts	Introduction 0 Socket Concepts	Download Java Netbeans
Set up of socket connection Socket, ServerSocket, accept Introduction 1 Socket Connection	Introduction 1 Socket Connection
Client-Server communication with streams InputStream, OutputStream Introduction 2 Socket Communication	Introduction 2 Socket Communication
	Introduction 3 Socket Communication
	Introduction 4 Socket & Threads

Web Service Tutorials

Description/Subjects	Tutorials	Exercises
Theoretical Web Service Concepts SOAP, XML, consumer, provider Introduction 0 Web Service Concepts	Introduction 0 Web Service Concepts	Download Java Netbeans
Set up of Web service consumer wsdl file, generated web services Introduction 1 Web Service Currency Consumer	Introduction 1 Web Service Currency Consumer
Set up of local Web service provider Web service class, operations, @Webparam, @Webname Introduction 2 Web Service Calculator Provider	Introduction 2 Web Service Calculator Provider	Web Service Calculator
Set up of Web service consumer wsdl file, generated web services Introduction 3 Web Service Calculator Consumer	Introduction 3 Web Service Calculator Consumer	Web Service Calculator
Catching SOAP exceptions SOAPFaultException Introduction 4 Web Service Exception Handling	Introduction 4 Web Service Exception Handling

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