Machine LearningWeekly Plans

Updated

ML(Machie Learning)
4th Semester: Datamatician Course (Computer Science)

This is a preliminary schedule: be prepared for changes !
CHECK ALWAYS THE RESPECTIVE WEEK

Week	Subjects	Litterature Slides/Videos/Links	Exercises/Solutions/Examples
4	Getting ready Zealand, Roskilde	Doonesbury 1-2 Superman, Hitman... Compulsory (ML) Aurélion Gérion: Hands-On Machine Learning Background readings. An easy reader. (ML Beginners) Oliver Theobald: ML Absolute Beginners Important library/documentation for Machine Learning All about modelling and algorithms, sklearn, sckit-learn https://scikit-learn.org/stable Important documentation for Python Syntax, semantic and functions etc. https://docs.python.org/3.9 Statistical visualization tool Graphs, plot functions, histograms https://seaborn.pydata.org/ Simple explanations on ML-models https://towardsdatascience.com Machine Learning Glossary https://ml-cheatsheet.readthedocs.io Matplot library https://matplotlib.org/3.1.0/tutorials/introductory/pyplot.html	Buying Books  NOW Winterbathing in the Roskilde Fjord Link to E-Book (ML) Link to Jupyter Notebook Videos and online literature (A rather comprehensive list)
5 MICL Online	ML The Landscape Types of ML Challenges Testing Validation Linear regression Install necessary SW Anaconda with Jupyter Spyder SciKit Python3 Python exercises	Literature/recommendations/Links aand more ML: Ch. 1, p 1-27, (27-32) Useful ML Ch. 1 links Slides/Videos Machine Learning 1 Slides (PDF) YouTube; Machine Learning Intro	Chapter 1 Assignments No. 1 - 14 Exercises Ananconda Installation Guide.PP Ananconda Installation Guide.pdf Python Basic No. 1 Jupyter Introduction Panda Introduction Importing DataSet Housing Homeworks Maybe Python Basic No. 2 Solutions Python 1 Solution Jupyter Solution Panda Solution HousingTest on DataSet
6 MICL Class	End-to-End ML Project Part 1 Explore data Project phases/checklist Frame the project Performance measures Get and Analyze data Visualization, graphs Correlations Python exercises continues	Literature/recommendations/Links ML: Ch. 2, p 35-62 ML Management Checklist ML Management Checklist (PDF) ML Management Checklist (htm) Useful ML Ch. 2 links Slides/Video ML End-To-End Project No. 2A Agenda ML Chapter 2A Slides (PDF)	Exercises Python Basic No. 2 Linear Regression Housing Ch. 2 No. 1 Solutions Python 2 Solution Linear Regression Solution Linear Regression Smart Solution Regression Performance Solution Housing Solution
7/8 MICL Class	End-to-End ML Project Part 2 Prepare Data Data Cleaning Transformers Feature Scaling End-to-End ML Project Part 3 Find the best model Training the model Fine tuning Launch best model	Literature/recommendations/Links ML: Ch. 2, p 62-84 Useful ML links 2 Standard deviation Slides/Videos ML Chapter 2B Slides (PDF) YouTube: Linear Regression ML Chapter 2C Slides (PDF) YouTube: ML Cross Validation	Exercises Regression Performance Linear Regression Standard Linear Regression Missing Data Housing Ch. 2 No. 2 Solutions Linear Regression Standard Solution Linear Regression Missing Data Solution
8 MICL Online	Classification Types of Ckassification Binary classification Multi classification Cross Validation Confusion matrix Precision/specificity&Recall ROC-AUC	Literature/recommendations/Links ML: Ch. 3 Useful ML links 3 MNIST Database Confusion Matrix Calculator Slides/Videos ML Chapter 3 Slides (PDF) YouTube: ML Specifity & Sensivity YouTube. ML Confusion Matrix YouTube: ML ROC-AUC	Exercises from week 6/7 to be continued and finalized Exercises Classification Chapter 3 Questions Classfication MNIST Exercise MNIST Data Set Solutions
9 MICL Online	Finalize exercises from week 7/8 Introduce Mandatory assignment	Literature/recommendations/Links Statistical visualization tool Graphs, plot functions, histograms https://seaborn.pydata.org/	Mandatory 1: Linear Regression (Word) Mandatory 1: Linear Regression (PDF) Mandatory 1: Data set
10 MICL Online	Follow up on Mandatory assignment		Mandatory 1: Linear Regression (Word) Mandatory 1: Linear Regression (PDF) Mandatory 1: Data set
11 MICL Class	Training Models Different Training Models Gradient Descent Bacth and Mini-batch GD Stochastic Gradient Descent Ridge vs. Lasso Early stopping	Literature/recommendations/Links ML: Ch. 4, p. 111 - 142 Useful ML links 4 Slides/Videos ML Chapter 4 Training No 1 Slides ML Chapter 4 Training No 2 Slides ML Chapter 4 Training No 3 Slides Gradient Descent (23 min) Stochastic Gradient Descent (11 min) MIT Traditional Blackboard Lesson(44 min)	Training Chapter 4 Questions Training Model Exercise
12 MICL Class	Logistic Regression Logistic concepts Probability Sigmoid the logistic function Cost function Multinomial Logistic Regression Softmax Iris example in SciKit	Literature/recommendations/Links ML: Ch. 4, p. 142 - 152 ML Beginners: p. 52 - 58 Useful ML links 4 Towardsdatascience.com Logistic ML-Cheatsheet Logistic Slides/Videos ML Chapter 4 Logistic Regression No. 1 Slides ML Chapter 4 Logistic Regression No. 2 Slides Logistic Regression (8 min) Logistic Regression Details (17 min)	Exercises Logistic Regression Questions Chapter 4 Logistic Regression Iris Exercise Logistic Regression Iris Program
12 MICL Class	Decision Trees Decision trees structure Gini impurity Entropy CART Cost function Instability & sensitivity Iris example in SciKit	Literature/recommendations/Links ML: Ch. 6, p. 175 - 187 ML Beginners: p. 98 - 114 Useful ML links 6 Slides/Videos ML Decision Tree Slides (PDF) Decision Trees Introduction(16 min) Decision Trees Missing Data (5 min)	Exercises Decision Trees Questions Chapter 6 Decision Tree Iris Exercise Decision Trees Iris Program
13 Online	Finalizing Decision trees Support Vector Machines (SVM) SVM concepts Linear SVM Hard and soft margin Non-linear SVM Polynomial features The kernel trick Computational complexity SVM regression Iris example in SciKit	Literature/recommendations/Links ML: Ch. 5, p. 153 - 164 (p. 165 - 173 extensive) Useful ML links 5 Slides/Videos SVM Slides (PDF) SVM Introduction (20 minutes) SVM Polynomial Kernel (16 minutes)	Exercises SVM Questions Chapter 5 SVM Iris Exercise SVM Iris Program SVM Moon Program Mandatory 2: The Iris Case (PDF) NOT this year!
14 MICL Online	Unsupervised Learning (UL) A very interesting subject for ML-lovers. UL Concepts Clustering usage K-Means Anomaly detection Image segmentation Prepocessing Semisupervised learning (DBSCAN) Customer  Case SYNOPSIS discussion in lesson 6 and 7	Supervised teaching in Unsupervised Learning Techniques Literature/recommendations/Links ML: Ch. 9, p. 235 - 255 (p. 255 - 258 extensive) scikit-learn.org: clustering scikit-learn.org: unsupervised-nearest-neighbors Useful ML links 9 Slides/Videos UL Slides (PDF) UL K-Means (8 minutes) Mandatory 3: The Customer Case (PDF)	Exercises Unsupervised Learning Questions Chapter 9 UL K-Means Exercise Unsupervised Learning Programs Ch. 9 Unsupervised Learning Customer Exercise Customer Dataset on Kaggle Mandatory 3: The Customer Case (PDF)
15	Easter Vacation	That means students catch up	Teachers prepare !
16 MICL Online	Artificial Neural Networks(ANN) ANN principles Perceptrons Backpropagation MutiLevelPerceptrons (MLP) MLP Classification MLP Regression Tensorflow Keras 3 x cases Playground.Tensorflow.org	Literature/recommendations/Links ML: Ch. 10, p. 279 - 308, ML: Ch. 10, p. 314 - 315, ML: Ch. 10, p. 323 - 325 https://www.tensorflow.org/ https://keras.io/ https://www.tensorflow.org/js/demos https://developer.android.com/.. https://keras.io/models/sequential/ Useful ML links 10 Slides/Videos ANN Basics Slides (PDF) MLP Slides (PDF) ANN 1. Introduction (19 min) ANN 2. Learning by Gradients (21 min) ANN 3. Backpropagation (13 min) ANN 4. Math Calculus !! (10 min)	Exercises ANN Questions Chapter 10 Tensorflow Installation Perceptron Iris Exercise ANN Programs Ch. 10 MLP Classfication Fashion Exercise MLP Classfication Fashion New Exercise Playground at Tensorflow Exercise Problems problems
18	Synopsis writing starts	Synopsis tips and tricks Here are some ideas: Tensorflow applied Tensorflow and Big Data Tools investigations and Interpolation Tools and Face Recognition Security in ML Ensemble learning Random Forest Dimensonal reduction Tensorflow & low level Python Convolutional Neural Networks (CNN) Generative Adversarial Networks TextRecogntion Recurrent Neural Network (RNN) Visual attention Online ML and IOT-data Android with Neural Networks Reinforcement Learning https://www.kaggle.com/datasets Voice regognition Bird songs Bird pictures COVID19 FakeNews Poison Mushrooms +1000 more datasets !!!!
19	Synopsis writing	Synopsis tips and tricks
20	Synopsis writing	Synopsis tips and tricks
21	Synopsis writing	Synopsis tips and tricks
22	Synopsis writing / hand-in
	Rehearsal for exam
23	Oral Exam Good luck !		Exam Roll List Re-exam unknown
33	5th Semester starts

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