Lec. |
Date |
Topic |
Lecture Material |
Reference |
Readings |

Introduction to machine learning. |
Slides, chapter-01 | Bishop section 1.1 | Computing Machinery and Intelligence Probability Review-1 Probability Review-2 Linear Algebra Review |
||

Regression and Overfitting. |
chapter-01 | Bishop section 1.1, HTF section 2.3 |
|||

HOLIDAY. |
Singular Value Decomposition. | ||||

Overfitting, ML Pipeline, Classification. |
chapter-01 | Bishop section 1.1, HTF section 2.3 |
|||

Statistical Decision Theory, More on Linear Regression. |
Chapter-02, Chapter-03 | HTF section 2.4, 2.5 Bishop section 3.1 |
Linear Algebra review (7.1) in this pdf Lagrange Multiplier (Appendix E in Bishop) |
||

Gradient Descent, Regularization. |
Chapter-03 | Bishop section 3.1, 3.1.4 HTF section 3.4.1, 3.4.2 |
|||

Bias-variance Tradeoff, Linear Classification. |
Bishop section 1.5.5, 3.2, 1.5.4, 4.1.1, 4.1.2 | Intro to convex optimization (page 91 to 102 in this book) | |||

Linear Classification, PCA, LDA. |

- [HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman.
*The Elements of Statistical Learning: Data Mining, Inference, and Prediction.*Available free online. - [Bishop] Christopher Bishop.
*Pattern Recognition and Machine Learning.* - [Mitchell] Tom Mitchell.
*Machine Learning.* - [TSKK] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar.
*Introduction to Data Mining.* - [Rojas] Raul Rojas.
*Neural Networks.* - [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville.
*Deep Learning.*Available free online.