Machine Learning for Large-Scale Data Analysis and Decision Making (MATH80629A): Winter 2023

Lecture Schedule

1- Week 1 (January 6): Class introduction and math review

Lecture: Slides
Reading:
- Prologue to The Master Algorithm
- Chapter 1 of ESL
- For Math review: Check here

2- Week 2 (January 13): Machine learning fundamentals

Lecture: Slides
Capsules:
- Learning Problem [14:40]
- Types of Experiences [13:15]
- A first Supervised Model [8:03]
- Model Evaluation [15:26]
- Regularization [4:09]
- Model Validation [3:08]
- Bias / Variance tradeoff [11:50]
Reading:
- Chapter 5 of Deep Learning. You can skip 5.4 (except 5.4.4) to 5.10.

3- Week 3 (January 20): Supervised learning algorithms

Lecture: Slides
Capsules:
- Nearest Neighbor [19:05]
- Linear Classification [15:26]
- Introduction to Probabilistic Models (for Classification) [11:55]
- The Naive Bayes Model [24:28]
- Naive Bayes Example [9:26]
Reading: Sections 4.1-4.3, 4.5 of The Elements of Statistical Learning (available online), Sections 3.5 and 4.2 of Machine Learning (K. Murphy)

4- Week 4 (January 27): Python for scientific computations and machine learning

5- Week 5 (February 3): Neural networks and deep learning

Lecture: Slides
Capsules:
- From linear classification to neural networks [19:28]
- Training neural networks [20:14]
- Learning representations [13:40]
- Neural networks hyperparameters [25:20]
- Neural networks takeaways [7:00]
Reading:
- Sections 6.1–6.3 and 6.5 (stop at 6.5.4) of Deep Learning (the book).
- Optional: Chapter 11 of the Elements of Statistical Learning.

6- Week 6 (February 10): Recurrent Neural networks and Convolutional neural networks

Lecture: Slides
Capsules:
- Modelling Sequential Data [8:42]
- Practical Overview of RNNs [29:32]
- RNNs for language modelling [15:13]
- Overview of CNNs [13:30]
- Convolutions and Pooling [26:00]
- Conclusions and Practical remarks [9:17]
Reading: Required readings: Sections 10, 10.1, 10.2 (skim 10.2.2, skip 10.2.3), and 10.7. Sections 9, 9.1, 9.2, 9.3 (9.11 for fun). Both from the Deep Learning book.

7- Week 7 (February 17): Unsupervised learning

Lecture: Slides
Capsules:
- Introduction to unsupervised learning [8:17]
- K-means clustering [41:58] (there’s a natural break at 22:28)
- GMMs for clustering [17:52]
- Beyond Clustering [14:42]
Reading: Section 14.3 (skip 14.3.5 and 14.3.12) of the Elements of Statistical Learning. Optional: Chapter 9 of the Pattern Recognition and Machine Learning.

8- Week 8 (February 24): Reading week

9- Week 9 (March 3): Project meetings

10- Week 10 (March 10): Parallel computational paradigms for large-scale data processing

Lecture: Slides
Capsules:
- Introduction to Distributed Computing for ML [19:35]
- MapReduce [17:41]
- Spark [17:37]

11- Week 11 (March 17): Trustworthy Machine Learning & Recommender systems

Lecture: slides
Reading: Chapters 1 through 4, Aggarwal, Charu C. Recommender Systems: the Textbook. Cham: Springer, 2016

12- Week 12 (March 24): Sequential decision making I

Lecture: Slides
Capsules:
- Motivating RL [8:22]
- Planning with MDPs [12:16]
- MDP objective [14:16]
- Algorithms for solving MDPs [17:51]: Note: In this capsule, there is a mistake in the second equation of the policy iteration algorithm (the transition should be given a and not π(s)), the slides have been corrected (see slides 47 and 48)
Reading: Optional: Demo of the policy iteration algorithm (from Andrej Karpathy)

13- Week 13 (March 31): Sequential decision making II

Lecture: Slides
Capsules:
- Introduction to RL [13:31]
- A first RL algorithm [17:13]
- RL Algorithms for Control [21:10]
Reading: Required reading: Sections 1 through 4 from this Survey, Chapters 1,3,4, and 6 from Reinforcement Learning: An Introduction. Optional: Demo of the TD algorithm (from Andrej Karpathy)

14- Week 14 (April 7): Easter Break

15- Week 15 (April 14): Class Project presentation