INF8953CE: Machine Learning (Fall 2020)

Tentative Schedule for Fall 2020

Lecture videos are available here.

Lecture scribbles are available here.

Lecture Material
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
Sep. 03

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

Singular Value Decomposition.
Sep. 10

Overfitting, ML Pipeline, Classification.
chapter-01 Bishop section 1.1,
HTF section 2.3
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)
Sep. 17

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