Class time: Saturday 13:00 – 14:50.
Office hours: Saturday 15:00 - 15:50.
Recommended Learning Resources:
An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie & Robert Tibshirani (Book).
Statistical Learning by Trevor Hastie & Robert Tibshirani (Online Course).
Elements of Statistical Learning by Jerome H. Friedman, Trevor Hastie & Robert Tibshirani (Book)
Machine Learning by Andrew Ng (Online Course).
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (Book).
Send your solution as a pdf file to my SDU email. File name format: name_surname_hw#.pdf (example: yernat_assylbekov_hw1.pdf).
Add your topic to this spreadsheet and ALSO send me an email.
IMPORTANT: Submit the topic of your course project by the end of April 9, 2022. Failing to submit your topic by the deadline will cause 5% subtraction from the overall course project % (20%).
Lecture 1: Introduction to the Course and Review of Linear Algebra (ppt) (pdf1) (pdf2) (video1) (video2)
Lecture 2: Discussion of Syllabus and Review of Calculus (pdf) (video)
Lecture 3 & 4: Review of Optimization: Basic Theory (pdf) (video1) (video2)
Lecture 5: Review of Optimization: Gradient Descend (pdf) (video)
Lecture 6: Basics of Machine Learning + KNN (pdf) (video1) (video2)
Lectures 7-9: Linear Regression + Logistic Regression + Softmax Regression (pdf1) (pdf2) (video1) (video2) (video3) (video4)
Lecture 10-11: Methods of Evaluation + Decision Trees (pdf1) (pdf2) (video1) (video2)
Lecture 13: Ensembling Methods + Clustering (K-Means) (pdf) (video)