INF8953CE: Machine Learning (Fall 2020)

Tentative Schedule for Fall 2020

Lecture videos are available here.

Lecture scribbles are available here.

Lec.
Date
Topic
Lecture Material
Reference
Readings
1
Aug. 31

Introduction to machine learning.
Slides, chapter-01 Bishop section 1.1 Computing Machinery and Intelligence
Probability Review-1
Probability Review-2
Linear Algebra Review
2
Sep. 03

Regression and Overfitting.
chapter-01 Bishop section 1.1,
HTF section 2.3
Sep. 07

HOLIDAY.
Singular Value Decomposition.
3
Sep. 10

Overfitting, ML Pipeline, Classification.
chapter-01 Bishop section 1.1,
HTF section 2.3
4
Sep. 14

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)
5
Sep. 17

Gradient Descent, Regularization.
Chapter-03 Bishop section 3.1, 3.1.4
HTF section 3.4.1, 3.4.2
5
Sep. 21

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)
5
Sep. 24

Linear Classification, PCA, LDA.

Future Topics

GLMs, GDA, evaluation metric for classification, Naive Bayes, Logistic Regression, Newton-Raphson Method, Perceptron, Separating hyperplanes, SVM, non-parametric methods, Decision Trees, Ensembles, Neural Networks, Training Deep Nets, Optimization, ConvNets and RNNs, MLE, MAP, Bayesian Learning, Bayesian Linear Regression, Kernel Methods and Gaussian Processes, Clustering, K-means, DBScan, GMMs and EM Algorithm, Frontiers in ML, What Next?

Reference Materials

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