Machine Learning
Lecture, BUITEMS, Department of Software Engineering and Department of Computer Engineering, 2022
2 + 1 Credit Hours - Fall 2022 Welcome to Machine Learning class! The course material and lectures will be posted on this site and students will be notified accordingly. Lectures ====== Reference Book(s): 1. [Pattern Recognition & Machine Learning, latest Edition, Chris Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) 2. [Foundations of Machine Learning](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/foundations%20of%20machine%20learning%20second%20edition.pdf) 3. Applied Machine Learning, online Edition, David Forsyth | **Date** | **Lecture No. (Download Link)** | **Topic** |**Assignment**| **supplementary content/ LABS**| |------------|----------------------------------------------------------------------------------------------------------------------|--------------------------------------|--------------|--------------------------| | 19/09/2022 (Week 1) | [Lecture 1](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/ML%20Lecture%201%20%20Introduction%20to%20Machine%20Learning.pdf) | Introduction to Machine Learning |[Homework 1](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/ML_assignment1.pdf)|[intro to ML, Google](https://developers.google.com/machine-learning/intro-to-ml)| | 26/09/2022 (Week 2) | [Lecture 2](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/blob/master/ML/ML%20lecture%20notes%201%202022-09-28%2017_52_03.pdf) | Linear Regression - Basic Equation|[numpy](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/numpy_ultraquick_tutorial.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=numpy_tf2-colab&hl=en) [pandas](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=pandas_tf2-colab&hl=en)|[Stanford YT video](https://www.youtube.com/watch?v=4b4MUYve_U8), [Google - Linear Regression](https://developers.google.com/machine-learning/crash-course/descending-into-ml/linear-regression), [simple regression and analysis in Urdu](https://www.youtube.com/watch?v=37oVtO3vU9Y&ab_channel=WaqarDar), , [correlation in urdu](https://www.youtube.com/watch?v=kEI8HAEsgKg&ab_channel=WaqarDar), [Multicolinearity](https://www.youtube.com/watch?v=sVJW5UXe84s&ab_channel=CampusX) | | 03/10/2022 (Week 3) | [Lecture 3](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/blob/master/ML/ML%20lecture%20notes%201%202022-09-28%2017_52_03.pdf) | Linear Regression - Loss Function and Gradient Descent|Homework 2 - Linear regression Code|[Linear Regression Lab](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/blob/master/ML/Linear%20Regression.ipynb), [lab zip file](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/Linear%20Regression.zip) | 10/10/2022 (Week 4)| [Lecture 4](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/KNN%20and%20optimization.pdf) | K nearest neighbor classifier |[Homework 3 - Classification](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/blob/master/ML/homework%2B3.ipynb)|[KNN lab](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/Classification%20with%20KNN%20(2).zip), [fruit dataset](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/blob/master/ML/fruit_data_with_colors%20(1).txt)| | 17/10/2022 (Week 5)| [Lecture 5](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/KNN%20and%20optimization.pdf) | K nearest neighbor Regression, overfitting and underfitting ||[K nearest Neighbors - medium](https://medium.com/swlh/k-nearest-neighbor-ca2593d7a3c4), [KNN regression code](https://gist.github.com/Saniya-Ashraf/43d6996a9313c118fd2607be89833107)| | 24/10/2022 (Week 6)| [Lecture 6](https://github.com/Saniya-Ashraf/saniya-ashraf.github.io/raw/master/ML/Regularization%20and%20SVMS.pdf) [Bias/ Variance Tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html) | Lasso Regression, Ridge Regression, Logistic Regression, Linear SVMs |Quiz in Lab class|[Logistic Regression](https://www.youtube.com/watch?v=yIYKR4sgzI8), Also please refer the notes for Linear Regression, [Support Vector Machines](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Bias/ Variance Tradeoff](https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229)| | 31/10/2022 (Week 7)| Lecture 7 | Kernels for SVM, Decision Trees ||[kfold vs leave-one-out](https://www.baeldung.com/cs/cross-validation-k-fold-loo)| |MIDTERM|MIDTERM|MIDTERM|MIDTERM|MIDTERM| | 7/11/2022 (Week 8)| Lecture 8 |Introduction to Neural Networks||| | 14/11/2022 (Week 9)| Lecture 9 | Back propogation and Activation functions||| | 21/11/2022 (Week 10)| Lecture 10 | XGBoost and Ensemble||| | 28/11/2022 (Week 11)| Lecture 10 | Unsupervised Learning||| **Updates** *Office Hours on friday 11am - 1.30am and 3pm to 5pm. *meanwhile for questions or scheduling a meeting, please email. *most slides and some labs are based on the course [Applied Machine Learning](https://www.coursera.org/learn/python-machine-learning).