# Machine Learning for Core Engineering Disciplines

- Institution: IISc Bangalore
- Department: Multidisciplinary
- Teacher: Prof. Ananth Govind Rajan
- Level: Undergraduate
- Language: English
- Subjects: Computer science, Engineering
- Source: [NPTEL](https://nptel.ac.in/courses/127108778)
- Licence: [CC BY-NC-SA](https://nptel.ac.in/faq) (Non-commercial use only.)
- Syllabus: [at the source](https://nptel.ac.in/courses/127108778)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

This course will introduce students and practitioners of core engineering disciplines to the fundamentals and applications of machine learning. The course will cover the following material: introduction to data science and machine learning, examples of supervised/unsupervised/reinforcement learning, motivation for machine learning with examples derived from various core engineering disciplines; introduction to Python, scientific computing packages (NumPy, SciPy, Matplotlib), and simple ML packages (Scikit-learn, Pytorch); linear and nonlinear regression, confidence intervals and goodness of fit, loss functions, gradient descent algorithm, overfitting/underfitting, regression/classification learning; clustering algorithms, singular-value decomposition, principal component analysis, and nonlinear dimensionality reduction; decision trees and ensemble methods, boosting and bagging techniques, random forests, gradient-boosted machine learning, support vector machines, Gaussian process regression; hyperparameter tuning and cross validation; introduction to neural networks and deep learning; feed-forward, convolutional, and recurrent neural networks

Intended audience: Core Engineering Disciplines

## Lectures

1. [Introduction to Data Science, Artificial Intelligence, and Machine Learning](https://invisiblenetwork.ai/learn/nptel/127108778/ad09f02a53d9) (YouTube)
2. [Fundamentals of Machine Learning](https://invisiblenetwork.ai/learn/nptel/127108778/6b98f0b7068e) (YouTube)
3. [Probability and Statistical Foundations for Machine Learning - I](https://invisiblenetwork.ai/learn/nptel/127108778/1c5422a578f9) (YouTube)
4. [Probability and Statistical Foundations for Machine Learning - II](https://invisiblenetwork.ai/learn/nptel/127108778/727b3ff2aa7c) (YouTube)
5. [Probability Distributions for Discrete and Continuous Random Variables](https://invisiblenetwork.ai/learn/nptel/127108778/25190e5c98ab) (YouTube)
6. [Transforming Random Variables and Their Distributions](https://invisiblenetwork.ai/learn/nptel/127108778/303cf9a9d92b) (YouTube)
7. [Understanding Population and Sample Statistics - I](https://invisiblenetwork.ai/learn/nptel/127108778/ec25c8a673c8) (YouTube)
8. [Understanding Population and Sample Statistics - II](https://invisiblenetwork.ai/learn/nptel/127108778/56e939a48d14) (YouTube)
9. [Multivariate Linear Regression and Model Evaluation](https://invisiblenetwork.ai/learn/nptel/127108778/bf2372a1b8c1) (YouTube)
10. [Hypothesis Testing and Confidence Intervals: Z-Test and T-Test](https://invisiblenetwork.ai/learn/nptel/127108778/7f190662db51) (YouTube)
11. [Overfitting, Underfitting, and Ridge Regression](https://invisiblenetwork.ai/learn/nptel/127108778/5437492a0538) (YouTube)
12. [LASSO and Elastic Net Regularization Techniques](https://invisiblenetwork.ai/learn/nptel/127108778/535123803a04) (YouTube)
13. [Bias-Variance TradeOff in Machine Learning](https://invisiblenetwork.ai/learn/nptel/127108778/43711ef9f2fc) (YouTube)
14. [Logistic Regression and Evaluation of Binary Classification Models](https://invisiblenetwork.ai/learn/nptel/127108778/9073129c3541) (YouTube)
15. [ROC Analysis and Multiclass Classification](https://invisiblenetwork.ai/learn/nptel/127108778/63351eaa1529) (YouTube)
16. [Tutorial - I (Introduction to Python)](https://invisiblenetwork.ai/learn/nptel/127108778/37055d97c599) (YouTube)
17. [Loss Functions - I](https://invisiblenetwork.ai/learn/nptel/127108778/5dec62c28d6b) (YouTube)
18. [Loss Functions - II](https://invisiblenetwork.ai/learn/nptel/127108778/d11ac9e39f25) (YouTube)
19. [Loss Functions - III](https://invisiblenetwork.ai/learn/nptel/127108778/99063c7e9f93) (YouTube)
20. [Loss Function - IV](https://invisiblenetwork.ai/learn/nptel/127108778/631fc8bc8e84) (YouTube)
21. [Training ML Models: Gradient Descent](https://invisiblenetwork.ai/learn/nptel/127108778/8fa8b81880e1) (YouTube)
22. [Training ML Models: Gradient Descent and Hessian Matrix Analysis](https://invisiblenetwork.ai/learn/nptel/127108778/112f69895c32) (YouTube)
23. [Practical Aspects of ML Model Training and Stochastic Gradient Descent](https://invisiblenetwork.ai/learn/nptel/127108778/11bf8c259d05) (YouTube)
24. [Variants of Stochastic Gradient Descent for ML Model Training](https://invisiblenetwork.ai/learn/nptel/127108778/c726d79e28e8) (YouTube)
25. [Gradient Descent Applied to Toy Quadratic Regression](https://invisiblenetwork.ai/learn/nptel/127108778/04d821c023a9) (YouTube)
26. [Cross Validation in Machine Learning](https://invisiblenetwork.ai/learn/nptel/127108778/f3477207e120) (YouTube)
27. [Hyperparameter Tuning for ML Models](https://invisiblenetwork.ai/learn/nptel/127108778/709491205a6b) (YouTube)
28. [Tutorial - II (Linear Regression using Python)](https://invisiblenetwork.ai/learn/nptel/127108778/6632d58bcc86) (YouTube)
29. [Tutorial - III (Ridge and LASSO Regression, Cross Validation and Hyperparameter tuning using Python)](https://invisiblenetwork.ai/learn/nptel/127108778/359ab56a20c2) (YouTube)
30. [Unsupervised Learning: Principal Component Analysis (PCA)](https://invisiblenetwork.ai/learn/nptel/127108778/9dd67c300b31) (YouTube)
31. [Singular Value Decomposition and PCA](https://invisiblenetwork.ai/learn/nptel/127108778/c8b0ee287a99) (YouTube)
32. [Fundamentals of Clustering with K-means](https://invisiblenetwork.ai/learn/nptel/127108778/519fef036c90) (YouTube)
33. [Advanced Clustering Techniques](https://invisiblenetwork.ai/learn/nptel/127108778/34b6c873a394) (YouTube)
34. [Nonlinear Dimensionality Reduction Techniques -I](https://invisiblenetwork.ai/learn/nptel/127108778/b43729ab5062) (YouTube)
35. [Nonlinear Dimensionality Reduction Techniques -II](https://invisiblenetwork.ai/learn/nptel/127108778/d2c587cb471b) (YouTube)
36. [Unsupervised Learning on Toy Datasets](https://invisiblenetwork.ai/learn/nptel/127108778/579c379153ba) (YouTube)
37. [Decision Trees in Supervised ML](https://invisiblenetwork.ai/learn/nptel/127108778/d809681b5f37) (YouTube)
38. [Decision Trees for Regression and Classification](https://invisiblenetwork.ai/learn/nptel/127108778/cf104f2afaa3) (YouTube)
39. [Classification using Simple Decision Trees](https://invisiblenetwork.ai/learn/nptel/127108778/1f276ecad2e0) (YouTube)
40. [Ensemble Learning](https://invisiblenetwork.ai/learn/nptel/127108778/68e763f327fc) (YouTube)
41. [Random Forest and Boosting of Decision Trees](https://invisiblenetwork.ai/learn/nptel/127108778/db5aa34db98a) (YouTube)
42. [Adaptive Boosting (AdaBoost)](https://invisiblenetwork.ai/learn/nptel/127108778/8148ef47d16c) (YouTube)
43. [Unsupervised Learning in Python (Tutorial 1)](https://invisiblenetwork.ai/learn/nptel/127108778/7a3845398145) (YouTube)
44. [Unsupervised Learning in Python (Tutorial 2)](https://invisiblenetwork.ai/learn/nptel/127108778/5e3438aa36db) (YouTube)
45. [Gradient Boosted Decision Trees and Advanced Boosting Libraries](https://invisiblenetwork.ai/learn/nptel/127108778/c2dad368337d) (YouTube)
46. [Decision Stump](https://invisiblenetwork.ai/learn/nptel/127108778/29af5fad854c) (YouTube)
47. [Support Vector Machines (SVM)](https://invisiblenetwork.ai/learn/nptel/127108778/ff3564418ddc) (YouTube)
48. [Mathematics of SVM Margins](https://invisiblenetwork.ai/learn/nptel/127108778/bf591d7885be) (YouTube)
49. [Support Vector Regression (SVR)](https://invisiblenetwork.ai/learn/nptel/127108778/b6fdcd3babc9) (YouTube)
50. [Kernel Ridge Regression (KRR)](https://invisiblenetwork.ai/learn/nptel/127108778/cedd0425c1be) (YouTube)
51. [Gaussian Process Regression (GPR)](https://invisiblenetwork.ai/learn/nptel/127108778/ea7ae57ba6f4) (YouTube)
52. [Supervised Machine Learning Tutorial with Python (Tutorial I)](https://invisiblenetwork.ai/learn/nptel/127108778/07e83d7474e8) (YouTube)
53. [Supervised Machine Learning Tutorial with Python (Tutorial II)](https://invisiblenetwork.ai/learn/nptel/127108778/87a743967cb7) (YouTube)
54. [Supervised Machine Learning Tutorial with Python (Tutorial III)](https://invisiblenetwork.ai/learn/nptel/127108778/092bccd33d19) (YouTube)
55. [Introduction to Deep Learning and Neural Networks](https://invisiblenetwork.ai/learn/nptel/127108778/f12dab6d1522) (YouTube)
56. [Overview of Advanced Neural Network Architectures: From CNNs to GANs and GNNs](https://invisiblenetwork.ai/learn/nptel/127108778/88488bf089a8) (YouTube)
57. [Mathematical Foundation and Activation Functions of Neural Networks](https://invisiblenetwork.ai/learn/nptel/127108778/4fa05375c025) (YouTube)
58. [Training an Artificial Neural Network: Forward Propagation, Backpropagation, and Hyperparameters](https://invisiblenetwork.ai/learn/nptel/127108778/9a7d5fa64b5c) (YouTube)
59. [Challenges in Training Neural Networks and their Mitigation](https://invisiblenetwork.ai/learn/nptel/127108778/b748594b9732) (YouTube)
60. [Neural Network Challenges (Gradients, Overfitting) and Logic Gate Implementation](https://invisiblenetwork.ai/learn/nptel/127108778/8e7a34e7e800) (YouTube)
61. [Recurrent Neural Networks and Sequential Data Processing](https://invisiblenetwork.ai/learn/nptel/127108778/5b9cf6dafdbd) (YouTube)
62. [Graph Neural Networks and Generative AI Fundamentals](https://invisiblenetwork.ai/learn/nptel/127108778/c1319bec01d4) (YouTube)
63. [Variational Autoencoders and Bayesian Generative Modeling](https://invisiblenetwork.ai/learn/nptel/127108778/bdc0716830fd) (YouTube)
64. [Neural Networks with Tensorflow (Tutorial I)](https://invisiblenetwork.ai/learn/nptel/127108778/e2face000667) (YouTube)
65. [Neural Networks with Tensorflow (Tutorial II)](https://invisiblenetwork.ai/learn/nptel/127108778/873617c7f97f) (YouTube)