Schedule

Instructor		Aritra Hazra
Timings [Slot: D4]		Monday (12:00-13:00), Tuesday (10:00-12:00), Thursday (08:00-09:00)
Venue		NC-231 (Nalanda Complex)
Teaching Assistants		Aritra Hota   |   Subhash Agrawal

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Notices and Announcements

March 20, 2025

The Project schedule is up! Please check and complete within the mentioned deadline (STRICT).

February 24, 2025

The evaluated answer scripts of Mid-Semester Examination will be shown on 27-Feb-2025 (Thursday) at 5:30pm (Venue: CSE-107).

February 10, 2025

The Mid-Semester Examination is scheduled on 21-Feb-2025 (Friday) afternoon (please check your admit card for locating your venue of the exam!).

February 07, 2025

The evaluated answer scripts of Class Test will be shown on 11-Feb-2025 (Tuesday) at 5:30pm (Venue: CSE-107).

January 21, 2025

The Class Test is scheduled on 29-Jan-2025 (Wednesday) at 6:00pm (Venue: CSE-119 + CSE-120). An email with all relevant details is being sent already to the registered students.

January 01, 2025

There will be an extra 2-hour class on 08-January-2025 (Wednesday) at 5:30pm in CSE-119 (CSE Dept., Ground Floor). An announcement for the same will also be made in the first class (tomorrow).

December 31, 2024

Wish you a very happy new year and semester ahead. Our first class will be on 02-January-2025 (Thursday) at 8:00am. An email with all relevant details will be sent to the registered/admitted students very soon.

December 19, 2024

This course is primarily designed as a CORE course for PGDBA students. However, research scholars (MS and PhD students) are allowed to participate in this course. Except these students, I am unable to admit any other UG or PG student to take part in this course. Hence, all requests from students sent via ERP will remain pending without being approved (and thereby those students are requested to switch into other courses before the subject registration deadline expires).

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Tentative Coverage

Module I : Algorithms for Data Science

Topic	Details	Duration	Dates	References	Class-Scribes* (Daily)	Additional-Scribes* (Weekly and Appendix)
Introduction	Problems, Representation, Solution and Landscape	1 hour	02-Jan-2025	CL[1] KT[2] HA[3]	– –	23BM6JP06_2025-01-06~10 (Week-1 Summary) 23BM6JP11_2025-01-13~17 (Week-2 Summary) 23BM6JP16_2025-01-20~24 (Week-3 Summary) 23BM6JP21_2025-01-27~31 (Week-4 Summary) 23BM6JP26_2025-02-03~07 (Week-5 Summary) 23BM6JP30_2025-02-10~14 (Week-6 Summary) 23CS91P01_2025-01-14 (BST and Heap) 24CS72P04_2025-01-16 (Matrix Multiplication) 24CS72P22_2025-02-04 (All-Pair Shortest Path)
Algorithm Design and Analysis	Recursive Formulation and Data Structuring Correctness and Optimality (Problem Lower Bounds) Efficiency (Time and Space Complexity)	6 hours	06-Jan-2025 07-Jan-2025 08-Jan-2025 09-Jan-2025 13-Jan-2025	KT[2] HA[3] MW[5]	23BM6JP01_2025-01-06 23BM6JP45_2025-01-07 23BM6JP03_2025-01-08 23BM6JP04_2025-01-08 23BM6JP05_2025-01-09 23BM6JP07_2025-01-13
Divide and Conquer Paradigm	Searching and Sorting More Example Problems and Analysis Master Theorem and Complexity Analysis	4 hours	14-Jan-2025 16-Jan-2025 20-Jan-2025	CL[1] HA[3]	23BM6JP08_2025-01-14 23BM6JP09_2025-01-14 23BM6JP10_2025-01-16 23BM6JP12_2025-01-20
Dynamic Programming	Classical Example Problems and Analysis String Matching Algorithms Memo(r)ization and Complexity Analysis	3 hours	21-Jan-2025 23-Jan-2025	CL[1] KT[2] HA[3]	23BM6JP13_2025-01-21 23BM6JP14_2025-01-21 23BM6JP15_2025-01-23
Graph Algorithms	Representation and Traversals Shortest Paths and Spanning Trees	4 hours	27-Jan-2025 28-Jan-2025 30-Jan-2025	CL[1] KT[2]	23BM6JP17_2025-01-27 23BM6JP18_2025-01-28 23BM6JP19_2025-01-28 23BM6JP20_2025-01-30
Intractable (Hard) Problems	P, NP and NP-Complete Problems Problem Reduction and Hardness	3 hours	04-Feb-2025 06-Feb-2025	CL[1] KT[2]	23BM6JP23_2025-02-04 23BM6JP27_2025-02-04 23BM6JP25_2025-02-06
Solving Hard Problems (Combinatorial Optimization)	Approximation and Randomized Algorithms Heuristic Search and Branch-and-Bound Approach Constraint Satisfaction and Local Search Simulated Annealing and Genetic Algorithms	4 hours	10-Feb-2025 11-Feb-2025 13-Feb-2025	CL[1] KT[2] HS[4] RN[6]	23BM6JP24_2025-02-10 23BM6JP28_2025-02-11 23BM6JP29_2025-02-11 TUTORIAL_2025-02-13

Module II : Foundations of Machine Learning

Topic	Details	Duration	Dates	References	Class-Scribes* (Daily)	Additional-Scribes* (Weekly and Appendix)
Introduction	The Learning Problem Feasibility of Learning	2 hours	27-Feb-2025 03-Mar-2025	TM[7] MI[8]	23BM6JP32_2025-02-27 23BM6JP33_2025-03-03	23BM6JP37_2025-03-03~07 (Week-7 Summary) 23BM6JP42_2025-03-10~14 (Week-8 Summary) 23BM6JP47_2025-03-17~21 (Week-9 Summary)
Decision Tree Learning	Entropy and Information Gain, Gini Index and Impurity Hypotheses Space and Inductive Bias, Occam's Razor Overfitting and Prunning	2 hours	04-Mar-2025	TM[7] EA[9]	23BM6JP34_2025-03-04 23BM6JP35_2025-03-04
Bayesian Learning	MLE and MAP, Naive Bayes and Gaussian Naive Bayes Bayesian Network and Graphical Models Expectation-Maximization Algorithm	4 hours	06-Mar-2025 10-Mar-2025 11-Mar-2025	TM[7] EA[9] CB[10]	23BM6JP36_2025-03-06 23BM6JP38_2025-03-10 23BM6JP39_2025-03-11 23BM6JP40_2025-03-11
Linear Models	Classification and Regression Non-linear Transformation Logistic Regression	2 hours	13-Mar-2025 17-Mar-2025	TM[7] MI[8]	23BM6JP41_2025-03-13 23BM6JP43_2025-03-17
Artificial Neural Networks	Perceptrons and Multi-Layer Neural Networks Back-propagation and Gradient Descent Deep Learning Architectures	2 hours	18-Mar-2025	TM[7] MI[8]	23BM6JP44_2025-03-18 23BM6JP02_2025-03-18
Support Vector Machines	Primal-Dual Optimization and Soft Margin Kernels and Radial Basis Function Dimensionality Reduction (Principal Component Analysis)	3 hours	20-Mar-2025 24-Mar-2025 25-Mar-2025	TM[7] MI[8] EA[9]	23BM6JP46_2025-03-20 23BM6JP48_2025-03-24 23BM6JP49_2025-03-25
Performance Evaluation	Accuracy, Precision, Recall, F-Score and ROC Sampling and Bootstraping Hypotheses Testing and Cross-Validation	1 hour	25-Mar-2025	TM[7] EA[9]	23BM6JP50_2025-03-25
Learning Theory	Error and Noise, Training vs. Testing and Validation Generalization, PAC Learnability and VC Dimensions Bias and Variance, Overfitting and Regularization	5 hours	27-Mar-2025 01-Apr-2025 03-Apr-2025 07-Apr-2025	MI[8]
Ensemble Learning	Bagging and Boosting AdaBoost and Random Forest	2 hour		EA[9]
Unsupervised Learning	Clustering, Semi-Supervised and Active Learning	2 hours		EA[9]
Conclusion	Core Learning Principles	1 hour		MI[8]

* Scribes are unedited class-notes documented by students (and uploaded as it is) based on the class teaching; and hence may contain errors!

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Books and References

Module I : Algorithms for Data Science

1. [CL] Thomas H. Cormen, Charles E. Lieserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Fourth Edition, MIT Press/McGraw-Hill, 2022.
2. [KT] Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.
3. [HA] Muhammad Hamad Alsuwaiyel, Algorithms: Design Techniques and Analysis, World Scientific Publishers, 2016.
4. [HS] Ellis Horowitz, Sartaj Sahni and Sanguthevar Rajasekaran, Fundamentals of Computer Algorithms, Universities Press, 2008.
5. [MW] Mark Allen Weiss, Data Structures and Algorithm Analysis in C, Pearson Education, 1997.

Module II : Foundations of Machine Learning

6. [RN] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Global Edition, Pearson, 2022.
7. [TM] Tom Mitchell; Machine Learning, First Edition, McGraw Hill, 1997.
8. [MI] Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin; Learning From Data, First Edition, AML Book, 2012.
9. [EA] Ethem Alpaydin; Introduction to Machine Learning, Fourth Edition, The MIT Press, March 2020.
10. [CB] Christopher Bishop; Pattern Recognition and Machine Learning, First Edition, Springer-Verlag New York, 2006.    [ Open-Access ]

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Scribes (Class-Notes by Students)

Scribe   [ Schedule | Submission-Link ]
(Duration: within a week after class, Marks: 20)

Projects / Assignments

Term-Project   [ Allocation | Submission-Link ]
(Duration: 21-Mar-2025 – 11-Apr-2025, Marks: 25)

Examinations

• Class Test   [ Question | Solution ]
  (29-Jan-2025, Wednesday, 6:00pm-7:00pm, Room: CSE-119+120, Marks: 25)
  [ Syllabus: Whatever covered under Module I upto Divide and Conquer Paradigm ]
  
  
• Mid-Semester   [ Question | Solution ]
  (21-Feb-2025, Friday, 2:00pm-4:00pm, Room: Central-Allocation, Marks: 60)
  [ Syllabus: Whatever covered under Module I ]
  
  
• End-Semester
  (24-Apr-2025, Thursday, 02:00pm-05:00pm, Room: Central-Allocation, Marks: 100)
  [ Syllabus: Whatever covered under Module II ]
  

Marks Distribution:   10% Class-Test + 10% Project + 30% Mid-Sem + 45% End-Sem + 5% Scribes